All righty, welcome back to Operating Systems. So people obviously have a midterm today. So we will keep this short and sweet. So this is pretty much this cleanup crew for scheduling and fun little things that you should probably know. So first we'll go back into scheduling and talk about a actual implementation of a priority schedule. We'll see what that looks like. So let's just get into it. We'll probably be done this lecture quite quick, and then you can go stay back for your other midterms and all that stuff. So let's explore dynamic priority scheduling, also in some literature called feedback scheduling. What this is, is it uses priorities and has the algorithm manage the priorities dynamically. That's why it's called dynamic priority scheduling. So we use set time slices, measure CPU usage during each time slice, and then the end of each time slice we recalculate the priorities of all the processes. Idea behind this is we increase the priority of processes that don't use their time slice, so they're more likely to execute in the next time slice, and we decrease the priority of processes that use their full time slice so they get shoved further back in the queue. So in this, we have to pick which number represents lower or higher priority, so we will do the thing Linux does and the thing that makes this algorithm work a bit better is the lower the number, the higher the priority. So each process, whenever it starts, gets assigned a priority P of N, and whenever we make a scheduling decision, we pick the lowest number or highest priority to schedule. If it yields or blocks or does anything, we will pick the next lowest number. If there is a tie, we will resort to a rival order, which you will always be told, and if a lower priority number or higher priority process becomes ready, we would switch immediately to it because we're doing preemption. Then for this, we record how much time our process executes in this priority interval, so you say calculate however long it executed before with the numbers C of N, and in this case, timer interrupts still occur, so the kernel maintains control. It could context switch whenever there's a timer interrupt, even though sometimes you don't want it to. So at the end of each priority interval, we update the priority of each process with this calculation, the new priority of the process N is equal to its old priority divided by two. Idea is that it gets smaller each time, and smaller number means more likely to get picked and scheduled. And then we add a constant, which is just however long it executed for in the last time slice. So after we add it, adjust the priorities, we reset our timekeeping variable C back to zero, and that is valid for the next time slice. So what's that look like? Well, let us assume all priorities have an initial priority of zero. That means they are all the same priority level, and we would try, and they're all tied. So if they're all tied, they go by arrival order. In this case, we have process X, Y, A, B arriving in that order, and A and B are CPU-bound processes, which just mean they want to execute on the CPU. If you give them any CPU time, they will take as much time as you give them. They just want to execute. They gotta go fast. X and Y are called IO-bound processes because they can only execute for a little bit of time before they block because they're waiting for some type of resource. So if they're waiting for a file to get read from the disk, something like that. So in this case, both X and Y can only execute for one time unit, and then they get blocked for five. So they won't use the CPU because they're waiting for some other resource. So in this case, timer error-ups occur every second. Each process can use at most 10 time units, and our priority interval is every time, 10 time units. Yeah, yeah, yeah. So in this case, after X executes for one time unit, it can't execute again for another five. So you may as well schedule something else. So it's waiting for something. All right, so here we have time slices 10. Priority interval is 10. So we will recalculate the priorities every 10 time units, and the time slice is an absolute maximum. I separated them out because we will see that, hey, we don't recalculate at the end of every time slice. We recalculate every 10 time units. So in this case, since they are all of equal priority, we go by arrival order. We would schedule X first. It can only execute for one time unit. So it is done at time one. So it would block for five, so we can't schedule it again for another five time unit. So it is available to be scheduled again at six. But we have to make another decision. We have to schedule another process. So next in line is Y. Schedule Y for one time unit, it executes. Now it has to block for five time units. So it is available after time seven. But in this case, we switch to process A, and it would execute for eight time units, even though at time six, X is ready, and at time seven, Y is ready. So we don't switch to it because they are of equal priority. If they are equal priority, we don't switch unless we absolutely have to. So even though they're ready, we don't switch to it, and we wait until time equals 10, whatever we have to recalculate the priorities. So if we recalculate the priorities, while their initial priority was all zero, so zero divided by two, all going to be zero. So their new priority is essentially going to be how many time units did they execute for in the last 10 time units? So the priority of X is one, priority of Y is one, priority of A is eight, and priority of B is zero. So now at time 10, after we recalculate, we can context switch in a new process. In this case, process B is the highest priority or lowest number, there is no other tie. So at time 10, we would just schedule it. It would run for the full 10 time units until we have to recalculate the priorities again, and then this would go on and on until eventually the process is finished. So also just as a note, if we didn't switch, our priorities here to have B as the higher one and A was a higher priority, while it would go for the rest of its time slice, so it would only execute for two more time units until having to context switch. But if it's still the highest priority, it would probably just be itself again and get picked again. So it would probably execute for all the way to 20. But in this case, B is the one that's picked, yay. All right, any questions about that? Pretty simple. All right, let's make this as exciting as we can. So we will change process A and B to have an initial priority of six. So they are lower priorities. So we would prefer to run X and Y first. So same thing happens. X and Y are a priority zero or the highest higher priority, there's a tie. So we have to, we just go to arrival order again. X gets picked, schedule it for one, gets blocked. Now in this case, we go to the next lowest priority number which is six, there's a tie between A and B. We pick A at this point because we go by arrival order. So we pick A, A executes for four time units until X is ready at six. And because it has a lower priority or higher lower number or higher priority, we would context switch away from A to B. It would context switch away from A directly to X. It would execute for one time unit, get blocked. Then Y would become ready. So we execute Y for one time unit. It gets blocked. And now at time eight, we haven't recalculated the priorities yet. The two processes we could run are A and B. They're of the same priority. So we have to do the same thing. We have to go by arrival order. So we would schedule process A to run until we recalculate the priorities. So here, if we recalculate the priorities, well, the initial priority of X and Y were both zero. So zero divided by two is still zero. And they both ran for two time units. So their new priority is two, Y same idea. And then for process A, its initial priority was six because we said so. So it's divide by two, three. And it ran for six time units. So its new priority is nine or very low. Process B executed for zero time units and its initial priority was six. So its new priority is three. So it doesn't get shoved ahead of process X or Y, but it gets a higher priority than process A. So in this case, at time 10, we can only schedule between process A and B because X and Y are both blocked. So we would pick B in this case because it is a lower number, higher priority. Get scheduled for two time units until process X is unblocked again. So it gets unblocked at seven plus five because we say it gets blocked for five time units. So at time 12, it's ready again. It is a lower number or higher priority than process B. So we would context switch directly to A. It would run for one time unit, get blocked. At time 13, Y is ready. It has a lower number or higher priority than process B. So it would get executed. And now at this point again, we can only choose between A and B. B is a lower number, higher priority. So we would schedule that. It would run for four time units until X is ready again. The next would run for one time unit, then Y and block.  And I could ask you to do this as mean as I want to be. Do it for 50 time units or something like that. Hopefully I'm not that mean, but you can see you just do this over and over again. It gets boring. Yep. So yeah, you can context switch away from any process at any time. But if it's blocked, you may as well context switch to something else. Otherwise your CPU is gonna be idle, right? If it's just blocked. No, so you can context switch at each timer and erupt. Yeah, so this is preemptible. So you can just preempt it at any time. Yep. Yeah, you just update them at each priority interval, not whenever you context switch. Because you wanna do things at a set interval because otherwise if we recalculated it, each time we context switch, we'd probably be wasting a lot of time if things switch really fast and we want it to be more predictable. Yep. If we can just like basically switch anytime we want. So usually the time slice and the priority interval are the same thing. So it's like the most it'll get run for until we recalculate the priorities and then it will probably get kicked out. So basically under this case, the time slice is only there to serve the purpose of keeping track of the priority interval? No, so in this case the time slice is only to keep track of however the maximum amount of time we can just have a process going at once. Yeah, actually yeah, you're right. In this case we don't even need to talk about the time slice because only the priority interval matters. So time slice maximum doesn't really matter because if it ends, it will have made the same decision until it recalculates. Yeah. Yeah, so in the previous example they were all tied for priority. So you wouldn't context switch at all if they're the same priority. So that's whenever you need to make a scheduling decision. So they just have this as if it becomes ready and it's not a higher priority number it just keeps, it doesn't bother to context switch. That's just a rule, yeah. Sorry. Yeah, so we'd recalculate all the priorities here and the priority of B would be three divided by two plus one, two, three, four, five, six, plus six. So six plus 1.5 be 7.5. All right, any other questions? Cool, oh yeah. So the time slice limit doesn't matter. The only thing that really matters is the priority interval whenever it recalculates priority. Because the rule is we'll always run a higher priority process over another one that is of lower priority. Yeah, you can context switch at any, in this case, any time unit you can context switch. So this is because we have priorities here, we're recalculating it. Before we just had round robin. So we just did time slice, we tried to be fair. We just had the time slice and we just alternated processes first in, first out. Yeah, yeah, oh, sure. Sure, you can have rational priorities or you could just scale everything by two if you want or something, I don't know. Or just round down and make it whatever, yeah. So these could be, you could use the same formula except just round down whatever you calculated if you wanted it to be whole numbers. Doesn't really matter. Yeah, sorry? No, in this case we only, we only recalculate the priorities every 10 time units in this case. Doesn't matter if they block, doesn't matter. Yeah, so your kernel would know that they're blocked like it's waiting for something. I can't execute it because it's waiting for some file or something like that. So they wouldn't ignore them when they compare the priorities. So remember process state diagram last time, there's like blocked and then ready and then running so they would be in the block state so they aren't eligible to run because they're waiting for something. So they're just, yeah, they still have priorities, we just can't pick them because even if we pick them we couldn't execute them because they're waiting for something. All right, cool, that's the content, yay. All right, fun content. So since we've done virtual memory now you know how to be a wizard. So large language models are a thing, people love them. So some are like gigantic 30 billion parameters. The model file itself is 20 gigs. So I must have to read the entire file but someone did a single line of code change. Okay, that's an embellishment. It was like five lines of code and they essentially shaved off like 75% of the memory usage and everyone thought they were a wizard even though they could have just taken this course and that's all you need. So in this, you can see the discussion, it's lots of fun. This is clickable if you want to read it. They're like, oh my, how did you do this?  Please impart your wisdom. So how this was possible is we are allowed to control our processes virtual memory. So there is a system call called mmap or memory map and it allows us to map files to virtual address to our virtual address space. And why is this useful?  It allows you to access the file directly as if it was just a big array of bytes. There's no need to set up your own buffers. There's no need for read and write system calls. In fact, most programs that are programmed by any NC that are sane at all use mmap for files. They don't use fopen. They don't use, oh well, you stuff to open it but they don't use read and write because they are a giant pain and they are super inefficient. Well, they're not inherently inefficient but if you want to make them efficient it's kind of difficult. So let's just dive into the example code. All right, so this program, all we're going to do is open a file. We're going to be meta because I guess that's the parent company, that's funny. We're going to open this mmap file with the read only flag. We'll use this open file, open system call. So we get a file descriptor out of it. We'll assume that it's valid. And then here is a system call that doesn't quite fit into anywhere and it is an fstat system call. So what that will do is fill out this struct stat for you and what this has in it is it has all sorts of information about files. So if you do LS something like this, well, these are the permissions of the files. This is a number that will figure out what it means later. This is who owns it. This is the group that owns it. This is how big it is. This is when it was last modified or something. I forget by default what it displays, but it tells you all information about a file. So this fstat system call will essentially fill in this structure with a bunch of fields and tell you all the information about whatever actual file this file descriptor represents. So the only one we care about here is st underscore size, which is just how big the file is. So in this case it is 538 bytes. So our M-map system call has six arguments and we don't need to use all of them. So the first argument is something that you will probably not ever use. It is an address. So if you had a preference for whatever virtual address you wanted to, you could request that the kernel set the virtual address to whatever the heck you want to set it to. Why would you want to do this? 99.9% of the time you will not have to do this, but hey, maybe you want to be very particular for some reason. I don't know, I don't always tell you what to do, but if you don't care, you can just set to null in which case the kernel will figure out whatever virtual address it likes. Next argument is the size. So how many bytes to make valid? And in this case, I say, well, make as many bytes valid, that matches the length or the size of the file. Next one is protections. So this is like the permissions for the page table entries. So in this case, I want to be able to read this memory. So there are other flags. I could also put like a right flag or an executable flag or whatever I wanted to. In this case, since I don't have a right flag, if I try and write to this memory, nothing will happen. The next one is real fun. So the next flag says essentially what should happen if a fork occurs. So the default you should pick is map private. And that means those virtual addresses behave the same as any old virtual addresses if you fork. So after you fork, both of them are completely independent. If you change some memory at a virtual address, it won't affect the other process. So that private means they're private for each individual process. If you wanted to, you could change this to shared. If you change this to shared, that means that is not true. That means whatever virtual address, if you use a virtual address given back by M-map and then you fork, if you change that memory in one process, it changes it in the parent process or vice versa. So it makes it so that that same memory is mapped to the same physical memory and those processes are sharing memory. So if you change it in one, you'll see it in the other. So this is another way of doing IPC shared memory. It is actually, once you figure out what the heck virtual memory is, shared memory actually isn't that bad. And it is a way to do inter-process communication because hey, two processes can now communicate with each other through some shared memory. In this case, we can set to private because it doesn't matter. All right, next argument is a file descriptor if I want to map a file. I could just request some memory and not give it a file descriptor, but in this case, I want it backed by a real file. So I will give it the file descriptor. And then this offset just says, how do we bytes into that file should I start this memory mapping? If you wanna map the whole file, you just start at byte zero, which is the beginning of the file. So after that, I check for errors and then my program comes real easy. After that, I don't have to do reads or well, in this case, I'll use printf instead, right? But I don't have to do any reads. Don't have to do any buffers. That file just looks like a big array of characters if I want to treat it as such. This case, I can just have a silly for loop that goes over every single character and then just prints that character just assuming that that's just a big old array. So if I go ahead and run this, I should get the contents of the file and I do. So I get the contents of the file because it just maps it to memory, makes it real nice, real easy. And then at the end of this, Lake Malik has a free M map, has a free and it's called M unmap. So everyone liked that, kinda cool? Yep, yep. So the file's loaded into physical memory but you don't really have to bother with it but that's the same thing. If you do a read system call into a buffer, while that buffer is in memory and in physical memory and that read system call is going to read information into that buffer, right? Ah, that's a great question. How does this save memory? So M map is lazy. So all it does is set up the page table, set aside some page table entries that will represent however much space you need and initially it doesn't even read anything from the file. So it creates an invalid page table entry during the M map call, sets aside some page table entries and because of that, it makes all the entries invalid. So it would get a page fault if you tried to access this virtual address before it does anything and because of that, the kernel can handle that page fault. So it would use the rest of the information in the page table entry to essentially have some bookkeeping information that says, hey, this page actually represents this file. So if I see a page fault on it, well, instead of passing that page fault back up to the program and making it SEG fault, I could be like, okay, I know what this address is supposed to represent. Then it could read from a disk and read that information into that page and then do the mapping and then it would have a valid mapping. So you only pay for what you use in this case. So yep. So if a file is smaller than a page, it'll still occupy a page because the kernel only cares about pages. So you'll have some of that space that you just can't use anymore. So in this case, whenever you access a new page of the file, that is when the kernel will read that data from a file. So if I only execute the first byte and the last byte, well, I would get a page fault whenever I access the beginning of the file, then it would read a page. So it wouldn't read just a byte, it would read a page from that file in the memory and then whenever I execute the last byte, well it would know how many pages it has to go and then only load the last page into memory and that's it. So I would just have to do two page loads instead of a file, I could map a 20 gig file and only use two actual pages. So 20 gigs, only eight kilobytes. How does math work? Yeah. Question is, can you M map a pipe? Probably not. Yeah, you could just say a size if you want. But yeah, I'm not sure if you can M map a pipe, but you typically do that for IPC in which case you just use shared memory anyways. So you can try, you can see what error you get out of it. You might, yeah, you've probably just got an error. So yeah, with this, this is nice. So it ensures that you only pay for what you use and you only pay for what you use, like whatever you read from the file and you don't have to manage anything. You just kick the can down the curve where you let the kernel deal with this. You don't have to do anything. If you had to implement this using read and write system calls and buffers, you would be there all day and you would probably make a mistake and then you would probably destroy the file or you would read something invalid and you would have to debug it for hours. Just let the kernel handle it. So if we go back to this question, that's what they did. That's what their entire fix was, is they just end map the file instead of reading the whole thing into memory. So that way they don't have to do anything special. You essentially just pay for what you use and it turns out whenever you do inference on this, well, they didn't use all the 20 gig file. They only use 6.8 gigs or something like that. So way better. Most models are really sparse so you don't use all the information for each inference. You only use parts of the model and turns out it works really well with this. So you can now be a wizard too. This doesn't really fit anywhere in the course but if you use files, just end map them. It's way better and you can actually explain why. You can probably, if you go in this discussion there, someone that took this course was talking about, well, isn't this wasteful too? How much space do you need for page tables in that case? Someone posted that you need 40 megabytes of page tables. Why did they get this number? Well, we are M mapping 20 gigabytes. So this is 20 times 1024, 1024, 1024, which is to the power of 30. So that just represents a gigabyte. So this is just how many bytes are in a gigabyte. And then they figured out that, hey, our page size is 4096. And then if I need to calculate how much space is being used for the page tables, I would multiply that by eight because I need a page table entry for each of these pages. And then they just divided by megabytes so they got a nice number. So 1024 times 1024 is just a megabyte. So someone said that this is the same as 20 gigabytes divided by the four kilobyte page size times eight bytes per page table entry divided by one kilobyte, which on the discussion was a typo because that should have been one megabyte. So is this correct? Is this the worst case where I need 40 megabytes for all the page tables to map 20 gigabytes? So yeah, so in this case that is, this is only for entries, right? Which would be entries in the L zero page tables. So this doesn't say anything about L one page tables. So we should be able to figure out how many L one page tables we need in the best case. So this isn't quite correct. This is the correct number if we're only concerned about L zero entries. So we would need 40 megabytes in the best case of L zero page tables. So this like gets into like some actually stuff of like why are you being this pendantic? This is like silly, but this is just to illustrate that, hey, we have multi-level page tables, so let's be accurate. So in this case, if we have 20 gigabytes of memory, well, we can figure out how many page table entries we need. We'll just keep it in the number of page table entries so we can argue about how many page tables we need. So this is 20 times two to the 30, so that is 20 gigabytes divided by our page size means we need 20 times to the 18 page table entries. So these are how many page table entries that we need to do virtual to physical. So those would be the page table entries you need across your L zero page tables. So in fact, if we figure out that, hey, how many entries can we fit in an L zero page table? Well, we can fit two of the nine entries in an L zero page table, which because there's only nine index bits. So in that case, we can just take the number of page table entries divided by however many entries are in an L zero page table or in any page table to figure out that we need 20 times to the nine full L zero page tables. So these full L zero page tables is equal to 40 megabytes because of course, if they're full, then each of these are four kilobytes and we get back to the 40 megabyte number. So the numbers kind of all jive together. So if we have 10,240 L zero page tables, we can calculate in the best case how many L one page tables we need. So each L one page table can fit at most 512 entries. So we can figure out how many full L one page tables we need. So we take 10,240 divided by 512, which means we would have to round up and we know here that we would need 20 full L one page tables. So we would assume that our process has an L two page table because you need a two and every process needs its own unique L two page table. So we wouldn't add that to the calculation. So in the best case, we would actually need 10,260 full page tables which if you multiply that by the size of a page divided by a megabyte, we get like the actually like get your glasses on. This is 40.04 or sorry, 40.07 megabytes of page tables, not 40. Why am I being this exact? I have no idea, but this is the actual best case because we would have to consider L one page tables if you consider everything. But hey, turned out the number is close enough. So why the hell did I do this? I don't know. I like doing weird stuff. All right, any other questions? So L two, you only have one L two page table and it would already be created for you. So in this case, it kind of makes sense too. So this, if I have 24 L one page tables, it would mean that, well, I need to use 20 entries in my L two page table that already exists. So I just point each entry. So this also makes a bit more sense too that, hey, in the L two page table, there's 512 entries. Each one of those essentially can map up to a gigabyte of memory. Kind of makes sense. So in this case, I use 20 entries, 20 gigabytes, makes sense. All right, any other questions? Or lab three or anything? All right, otherwise we can end early and have good luck with your midterms. Yeah, yay for reading week being like super late and us trying to do a midterm after reading week. So, oh, well, all right. Well, just remember, point for you.


Welcome back on the water fishing freaks. It is time to do some harvesting. Two baits I want to focus on here today and talk about the specific situations and times a year that I throw them, but that's going to be a spoon and a rip bait. And we're really going to focus on the spoon more. The spoon for white bass is incredible. This is a bass quality spoon and I usually throw a spoon for bass in the colder months. So the winter time and in the fall, it can be pretty decent in the summer for bass, but it's better in the colder months in my opinion. But for white bass, it is a smasher. And I've been throwing spoons like this since my earliest memories of getting into fishing when I didn't have a boat, going to the bank, looking for white bass that were coming up, schooling on shad and throwing this out there. This is a lure I could throw the farthest and that's why I started using it. But you can reel it or you can use it like we're going to do today and fish some offshore points and humps. It's like middle of the day right now. The top water action close to the bank on the shad spawn is fizzled out and now the fish are starting to move out on those shad and get on the big points and humps and things like that. So let's tie one of these on and I'm going to show you the situation that I look forward to throw these. All right, we are hooked up. I think it's going to be a catfish and it is. Got a catfish wrangled. I love eating these blue cats. These chased around shad too. Let's see if I can get them in here without busting my line. Here we go. First fish landed. I will take these catfish all day long. They are so dang good. I just figured out a really good blackening technique too. I'll have to show you guys. These are shad eaters too. They will follow around white bass, large mouth, hang under the schools and they will clean up basically the dying wounded shad that the white bass and other bass hit. So I'm going to put him on the stringer and throw him in. First cast guys, we got a blue cat. All right, so this hump that I'm fishing I'm just keeping away about 30, 40 feet from the top of the hump and what you really want to look for in specific are fish that are on top of the shallows part of the long point or the hump. If you're seeing fish on top of that, that means it's dinner time. The dinner bell's going off. They're off to the side. They may not be feeding. I just had another one. But if they're on top, it is go time. And sometimes they'll just blend in.  Another thing that you might notice is fish will just kind of disappear off of it for a few minutes. You won't see them, but if you stick there long enough they'll just be waves of fish come on top. And when you see them, you want to get your lure out there. So I see that there are some big blue cats hanging on top. They might have pushed some of the other fish away and you'll see bass do that same thing with big large mouths come in there. The white bass will scatter around because they don't want to get eaten. So I'm just going to kind of do a little double pop. It's probably my favorite cat. It's just one, two. Let it hit the bottom. You'll know when it's on the bottom, your line will just go slack. Hook dip. Here's a little one. I think you'll quite measure. We'll throw him back. I'm going to do something right here that normally don't do for bass. Actually never. But I'm going to pinch my barbs down. So I'm using a glass rod. I've got 10 pound line. It's a nice soft rod. So medium power, really not concerned about losing the fish. I'm just concerned about my fingers and making this more of an efficient catching operation. Feels like a good one. Yeah, buddy. Yes, sir. That one's going to keep you, sir. You're the lucky first one on the chain. We're putting them on the ice. All right, we got a kitty cat. We've broken the seal on the keeper white bass. As long as we don't get stormed on, I think we're going to smash. So like that one, he picked it up when it was on the bottom.  Now I got those barbs pinched down. So it makes it a lot easier getting them off. The half ounce is what I'm going to throw most of the time. Just a real good weight for getting down there quick. And, you know, this is 22 feet of water. 20 to 30 feet of water is perfect. Oh my gosh, y'all. He just loaded. He's loaded up out of here. Well, bam. Smoked it. There's a little better one. Got a good tug on him. Got to love it. Oh yeah. Keeper ate it down the hatch. Normally that would be pretty tough to get out, but no barbs, get them out. Just my hands. All right. Number two going on the stringer. See how this almost looks like the bottom? Looks like the bottom is moving. Those are all fish. So I can literally just throw it right out in front here. And on your 2D, it's going to look a little different. You're going to see more like that. But the reason I know that they're white bass, the white bass are always moving up and down. Just got hit there. And he's on. Come on. White bass are always moving. Those little spaghetti lines, bananas. Always on the move. Oh yeah. Hooked up. Come here, my friend. You are going to keep. This is happening quick, y'all. There's a catfish right there. That's a catfish. So I'm going to drop down on him. See if I can get that blue cat. White bass are hitting it. You can see the school just following it up. It's just a tiny one though. Oh, they're going crazy for it. There he is. I just cannot, I cannot resist the spoon. Little small.  Throw you back. Quick work. Oh my gosh. Smoked. Smoked. That's a good one. Come here, friend. Oh yeah, you are keeping the meat necklace. Oh man, this is silly. Oh my gosh, y'all. We're just all on top of the herd. I just, I didn't even move it. I didn't even move it. As soon as I got down to the bottom, whoo. Smashed. Little guy. And this is why right here in this moment, no barbs is your friend. Easy. Go on. See this big herd coming from underneath. There goes the spoon down. It only takes a few seconds to get down there. And you can see, I don't, not even moving it. They're just attacking. Caper. Boy, it actually went through his gill plate. Just gonna cut that three times. Hello, my dear. The white bass or something absurd right now. Pretty absurd. I think they're, if Amy's feeling up for it, she might have a good time. She was just wondering what she's gonna do next. All right, we'll see you down there in a little bit. Just let me know when you get there. So I've been wanting to get my daughter on a wad of fish that's pretty easy. She gets a little freaked out when the fish is bigger and starts to pull really hard. These are perfect size. And all we gotta do is drop it straight down. I think she's gonna have a good time. Some day soon. Be ready for the dangle. All right, let me help you get over there. All right, watch out for the lures. Watch out for the hooks. Okay, first thing we gotta do is get you a little bit heavier lure title, okay? I'm gonna make a suggestion that I'm gonna let you pick. I really like this one right here since it's a real pretty bright color. Is it heavy? It's heavy, I feel it. It's sharp too. You're okay, it didn't get you. It didn't get you. Yeah, watch out for those sharp hooks, girl. Okay. All right, guys, we're gonna put on the same spoon. I was using this one, doesn't have a swivel on it, but it's a glow in the dark color. All you're gonna do when we get to the spot is you're just gonna drop it down like this. Let it get the bottom.  Wait till the fish gets on. Think you can do that? All right, I mean, I know last time we didn't catch anything. I know. But I got a good feeling about today. We can't catch them. We're gonna catch them. You're gonna catch them.  We're gonna double up.  No. Okay, good. Should be in good shape. All right, we are actually gonna hit a little bit different spot here. Different hump and come up and check it. See what we got going on here. And I see a couple. Oh, you're gonna relax? Nice and easy. That would get sweet. Nice and easy. Until we see a one. Work it like this, go. And then down. Up and then down. You gotta hold on to it. I think you got one. Yep, I do. You sure do. Uh-oh, might be a big one. Yep. Hold that rod over the side. There you go. Yeah, I got one. Look at that, relaxed position. What do you think it's gonna be? Fish. Turtle. Uh-oh. Turtle. I mean, you did that all by yourself, girl. All right, it's over the side. Here we go. Oh, Emmy girl. It's a fish egg. It's a fish. Whoa. Oh, it's a spit up something. It's spit up a shad and it's in daddy's shoe. Look inside of daddy's shoe. Daddy in the shoe. Look this out. Proud dad moment. Just let her do it. Let her do it by herself. She worked it, she got bit, she reeled it in. You know, next phase, getting it off the hook. I think there's something on there. You can try bringing that in. You want me to hold your snack? You feel something? I think you got one. Where's it going? Uh-oh. Yep, you got one. Yep, that's a keeper. Emmy girl. Emmy girl, that's a good keeper. Yep, yep, you're bringing them in. Just hold that rod over the boat, over the side. It's almost in. Hold your rod tip up. Hold it up. Hold your rod tip up. There you go. Oh my gosh, Emmy. Not throwing that one back. I thought you were gonna be so pumped to catch all these fish. I think we'll go nicely in the sandwich. Kimmy, hand me that measuring board, please. Almost a letter. Okay, daddy's gonna cast it for you. All right, now he's gonna do all the tough part. Hey, come on, get ready, get ready. Just gotta work it a little bit. Okay, you don't like that part? Let me just get a bite. I've already started it here for you. Here you go, reel it in. Reel that thing in. You got this? Whatever I got, I can reel it. You got this. Hold that rod up. Hold that rod up. That one's pulling a little bit, huh? Hold that rod up. You got it. What happened? Oh no, you pressed the button. Keep reeling, keep reeling. Dad, pop me the nose. Oh, it popped you in the nose? Yeah. Sorry, sweetheart. Whee. You hit the release button. I'm sorry, sweetie. You got a little something big here, I think. It's cool. Okay. Oh, Amy, you got a big old catfish. It's a big old catfish, Amy. Oh my gosh, Amy, hold the pole. Oh my gosh, it's a big catfish, and it got you, it got you in the nose. There, daddy's gotta lift it up. Daddy's gonna get it. Oh, that one felt a little different. Oh, he's talking.  There we go. That's gonna be a good dinner. Mama's gonna love that. Black and catfish, whew. Oh yeah. I want you talking. What's he saying? Honk-a-honk-a. That's a big one. High five. Had a little, had a little booboo on the nose? Yeah. All right, you can't press the thumb button while you're reeling in. Okay. Good job, though. I'm proud of you for sticking it out. I have to tell mom. Yeah, we'll tell mom. All right, fish and freaks. The spoon, Amy and I, we just crushed it on the new googin' spoon, on the slurpin' spoon. She caught fish all on her own, got two big bonus catfish out of it, and we've got a good little white bass to make our little, little cakes. If you guys haven't tried the cakes, go check out that recipe. I did a video on it. They're great with white bass, croppy, fantastic. Give them a shot. Amy, I'm proud of you, girl. What are you doing? I'm doing this.  Yeah. Probably all sorts of little jewels under the console. Successful trick. All right, we're gonna head out. Little Ben, welcome aboard the Silver Bullet, son. Maybe next year. Maybe next year for you. Oh, he's still living. Look at him, he's still trying to breathe. Oh yeah. We got a gold-fast root necklace full of him. And look at those two big KitKats. Oh my gosh. And he caught the bigger one. This one? Oh my gosh, Amy. Yeah. Look at that. High five. Woo, that was a good one. Ben, you wanna give me a high five? Oh yeah. Might as well get one from Mom too. Mom likes dinners. I do, I like dinners. She likes free catfish. Get on to catfish. I've never been a catfish person, but last time, the last one you cooked. Oh, it was amazing. So I'm gonna have you do it next time. I will, we will definitely do that. We'll show the folks at home. Good times with the fam, y'all. Before we sign it off today, I gotta give you a chicken check. Let's check up on the roost situation. Aren't you a happy rooster? Yes, sir. Still got a little bit of an eye problem, but he's getting over it. Had a little issue a couple days ago with a rat snake that actually got in here, but I moved, that's a little berry white. I moved her into her own nest and now she's sitting on 11 eggs. We had a dozen eggs, but rat snake got into the main coop, ate two, and then went into her little house and ate another one. But I moved her at night and so she's stuck. She is definitely glued to those eggs now that she basically was trying to fight a snake off. So she's got a lot invested in those eggs. Couple more weeks, we should have a couple of chicks hatching out of there. And just another update for you.  So hopefully here in a few months, we'll have some squash, okra, other stuff growing in there, and we'll have some sides to go with our fresh catches from the lake and the slab spoon as an offshore bottom fishing lure. And my book is still undefeated. You guys will be able to get your hands on the slurping spoon soon. Go to squad.com, use code LFG, save 10% off everything on there. So go check them out, use that code, and thank you guys for tuning in. You know what to do. See you on the next one. We will catch you on the next door adventure. Good job.


Today's video is kindly sponsored by Amazon Fresh. Good morning, everyone. Welcome back to the channel. The kitchen has been painted, and let me tell you, I have been living with this kitchen painted in its current state for about three days, only three days, but I am in love. I don't think I could have selected a better color for this kitchen. I cannot picture the cabinets any other way. There is something so magical about this view right here, just like the depth and moodiness of the lighting on the cabinetry. I wish that you can actually see it how I'm seeing it in person, because there is a little bit more warmth to the cabinets. So of course still needing the hardware, but the paint, I just wanted to let cure for a couple of days prior to even thinking about hardware. So this is what it's looking like. Oh my gosh, I also set up this little breadboard this morning with some faux bread on it. This is actually foam bread. I got this in Cleveland. It's all I could put there for now, but it's turning out really, really cute. I'm so excited to also add the lanterns that we have, but I 1,000% wanna go check out some hardware options because now that we have the cabinets done, they are painted, we can totally add hardware to those cabinets. And I wanna see what options that we have. I haven't been able to utilize Liz's anti-cardware for her anti-cardware yet until now I finally had something I'm able to use it for. And I wanna go check that store out. So I'm gonna head over there quickly and see what kind of hardware they have. I also need to count how many cabinets we have so I can figure out what we need in terms of poles and what we need in terms of handles. I also sadly broke this cabinet door when I was installing them. Look at it. I think I can fix it. I think I'm just gonna like put some wood glue in there, clamp it back together, and then repaint it. I think we'll be okay. So this is the newer hardware that they have. They have so many options of hardware. And then on the other side of the store is all of the anti-cardware. So over here, love these little star back plates.  Like a little knob to go on top of, kind of like that. I'm definitely wanting to do cup handles on the drawers. I just always love a cup handle. I think it's much more elevated than just like your traditional, you know, knob, like just a singular knob. So I wanna do cup handles. I am leaning more towards like a brass. This is so pretty, like the burnished. Look at all of these options. They have some that are a little bit more on like the rustic side. Oh, these are literally just like little kind of poles that you do. Some brass ones here that are super pretty. These are all antique, but they did say that they're not sure if they would have 18 drawer poles for a kitchen. Look at these little cube guys. Just kind of like sit on the edge and you just pull it. Not having too much luck here. Sadly, a lot of vintage handles, of course. You don't know how many there's gonna be. So I found 12 of one, but I need 13 of one that I really like. So I think I might have to do a little hunting online and seeing if I can find more quantity. I couldn't find a knob I loved in the newer area. I just feel like I can order them on Amazon if I'm gonna order something new, you know, probably cheaper. Just gonna try to find some cup handles and some other handles for the cabinets. I'm currently on my way to the grocery store. I decided that I wanted to pick up some groceries for the new house because I have consistently been there every single day and I have two full fridges completely unstocked with absolutely nothing inside of them. But now that the floors are redone and the marble is in, I'm going to be there probably every day, morning to night. So I wanna go ahead and stock up the fridges just a little bit so I have some food. Turning into the Amazon Fresh grocery store. Today's video is kindly sponsored by Amazon Fresh, which is super exciting. It's a full parking spot. Today I'm shopping at the Amazon Fresh grocery store in Encino, which is one of my favorite grocery stores. It is such a unique, innovative idea.  You literally walk right out of the door. You just scan your code as you walk in and it checks out for you. But I will say that Amazon Fresh grocery stores have such a vast selection of absolutely everything your traditional grocery store has, plus more with the convenience of simply walking and loading up your car and heading out. So when I was grocery shopping, I was mainly looking for items that I wanted to use in the house. I needed a little bit of toilet paper for the house. I also needed of course some pumpkin spice latte because why not? But I just grabbed a bunch of snacks. However, I personally am going to be shopping at Amazon Fresh so much this holiday season, especially because I really would love to entertain this season. I haven't gotten the opportunity to host at my house. And since I have my own house this year, which is so freaking exciting, I'm really wanting to host this year. And Amazon Fresh is the perfect grocery store for all of your hosting and cooking needs for any festivity that you're having. And also not to mention that they have the largest variety of pre-made and pre-cooked food, you guys. Like if you're going to a holiday party this year and you need something last minute, this is a great place to stop and get a full on pre-made dish for your holiday party. Even if you're hosting at home and you need something last minute, I'm sure you know the stress of the grocery stores around the holiday season. They are intense. They can honestly make you actually miss your holiday with how long you stand in line. So make sure to check out Amazon Fresh.  And if you need any help while shopping, you can just ask one of the Amazon Alexa's. Hey, Alexa. How many ounces are in a cup? One cup is eight fluid ounces. Now something really fun about shopping in store at Amazon Fresh is that you actually don't even have to check out. You literally put all of your items in your bag in the car and you walk out of the store and it checks out completely for you through your Amazon account. Definitely makes the process a lot easier. You know, waiting in lines. You don't have to back things up. Pre-done, which is really nice. So if you've never tried out Amazon Fresh before, you should definitely click the link in my description box below to find an Amazon Fresh store near you. And if you are a prime member, you could shop online with delivery. Morning, you guys. It's Long Beach Flea Market Day and you had, oops. You know I had to come. Bright and early, the sun is blinding me at the moment but we are gonna head into the Long Beach Flea Market. I'm here with Brittany from Badlands. She's right here. We're gonna go try to find some really, really good chic decor. We almost didn't come. We almost didn't. We almost did it, but then we're like, we have to. It's once a month. Like we- We're rallying. Okay. Paired. Paired for all the items. What are you doing? I need to put my invisible line in so I brush my teeth. Brittany got me these little glasses. Is this a housewarming gift? Yes. Oh! You brought these up because of the amber glass and the pendant lights. They're so cute. They're so cute. Yeah, I love them. All right, we walked in and the first thing, I already found two things I kind of like at the spoof.  It's really pretty though. It's very dainty. Yeah, it is dainty. Look at this fireplace screen I just found. It's like all iron. Look at this light, you guys. That's a fun one. Ooh, look at these wall sconces. Scandalabra wall sconce. I'm also currently in the market for some copper cookware to put in my kitchen. So behind the range that I'm gonna be getting, I'm adding one of those brass bars and I would love to have some copper cookware to add to that section. These are really, really cute. 90 for the set, guys. I ended up getting it. These are really, really pretty. I love the mix of the brass with the copper. Guys, I think I need this unique box with false bottom.  That is so cool. I think I need this. I think so. Surprise! Look at the drawer! Love it. So I got this one and I also ended up getting this one. I love this one, like for a shelf and I got it for $20. Look how pretty. Okay, you can actually see it now. Got a little overcast-y. The sun makes it hard to see some of these items, but how gorgeous is this sconce? Wall mounted. Oh my gosh, look at our items. We're already getting some cute things. I love these. Literally fill your card up, Brittany, every time. Brittany brings her own car and I fill it with my items and she's pulling my items. I'm sorry. I love it. It's so cool. The shape, we have no mirror so I'm gonna film a clip and show Brittany the shoe. It looks really nice on you. I just saved the day. Brittany found her phone and she's currently freaking out because she's in LA for a little design project, but I just found it. So I'm gonna go surprise her and tell her I found it. I don't know how I found it. I went to like a couple of the booths we were at and I happened to find it. We've been looking for like an hour. Let's see what she does. It was at the booth with the big pot. Oh, where I got my bowl. Yeah, your bowl. Mm-hmm, your bowl. Your life saver. Look at this like window piece, you guys. I love architectural salvage. Antique ceiling tin. Cutie. Here, put your feet up. That's like a reading lounger. It's like a great chair. View like the actual like side profile of it. Cutie. It's perfect. Oh my gosh, you just knocked over my iron scant.  Oh no, I think you've got a little bit of tetanus from that. Oh my God. Oh no. I heard McKenna's in the area. There she is, I see her. That's actually really cool. We have our antiquing group. McKenna and Brittany. We're going. See something promising. Oh, wow. Beautiful. Oh wow. These are so pretty. This leaded glass cabinet is beautiful. And this stained glass, I love that one. Pretty. I love. So cute. What do we think? Is this a fox or a wolf kind of? He's got a long nose. He does. But I think he's acceptable. It can be foxy enough, right? Yeah, that's a lone fox. Lone fox knocker. Like I don't know, but I, even like on a piece of furniture or something. I don't know, just like to put somewhere. Oh my gosh. He's so like floppy. Oh, do you need a new mommy? Look, he loves me. He's so cute. What are our thoughts on this? It actually matches my bag. It does. Oh my gosh. It's literally the same exact. Whoa. But it's. Wove, the wove. Is it comfortable? It actually is, it's very comfortable. It kind of squished down like when you sat in it. Would you like to take a seat? I would. Oh, isn't that pretty nice? The color is just so pretty. It's so like rich looking. Yeah, I hope you had two of them. I know, really great. I got a deal. I ended up getting this ottoman here, but then when I went to go check out, look at what I found. Look at this chest, you guys. How stunning is this chest with the little Spanish details and the metal up here. It even has the original key. He said this is from Spain. He sold me this, you guys, for $300, which I feel like is such a great deal. It is humongous. Like these iron handles on the side. Look at this original forged key. And the inside is so pretty. I need to open it. Look how big it is. It's so cool. I don't know what to put in there, but I'm gonna store something in there. I'll store you in there. He's literally carrying his chair. I got my items. So excited to share with you some of the new items that I got yesterday at the Long Beach antique market. And also today, Brittany from Badlands actually delivered a couple of pieces that I ordered from her store quite a while back. So when I purchased this house, me and Brittany hadn't met yet. And I actually ended up selecting a couple items from her shop that I wanted from my home. She held them for me until I was finally ready to kind of move furniture in and they are here. And you guys, here are some chairs. I am so excited to share with you the pieces. So I was literally not planning on going to Long Beach antique. It is once a month, but the night before Brittany texted me and goes, you know, Long Beach is tomorrow. Like, are we going? And I had a full day plan to filming. I was gonna be working on the house, but I was like, you know what? This is once a month. Like I should go. I still do need so much stuff for the new house. So I was like, well, I'm gonna go. We're gonna go. Let's see what we can find. And I'm glad I did because I did find some really, really great pieces. First item I got yesterday was this chair over here. Now from this view, it looks like a casual chair. However, from the side, I've been informed that it does look like a wheelchair.  And now everyone tells me that it looks like a wheelchair. I have to get rid of the chair, which you guys, I love it. It was $150 because that was a great deal. I thought the shape was really unique. I would love to know your guys' thoughts though. Would you use this chair in your home or would you not? Because I'm at a point now, like I wonder if I should reupholster it. I also feel like this blue medical color does not really coordinate well with the wheelchair vibe of the actual chair. So that could also be a reason why maybe when I actually paint in here it'll look a little bit better. But that is a chair that I got. I actually ended up getting two boxes as well. So I got this box here, which was $20 or 25, but I got two from him. So he ended up lowering the price. This one is just like a wooden box, but I loved the tone of it. Just like, you know, a storage box. Could put it on a shelf, could put it in a closet or something and actually utilize it for storage. And the other box was this fun toolbox. Which again, this was something that I was kind of picturing going on top of like a dresser with some flowers in it. Kind of like creating compartmentalized sections in here. Then using it differently as a toolbox. More so as like a wooden display box. I had these really, really cool pull out drawers. So it has a false bottom to it, which I thought was so unique. There's like little cubbies in the drawers as well. And then you guys also saw that I got some of that copper cookware. So I got this larger pot here, which has the brass handle. I think this is so pretty in great condition as well. Got the larger pot, this kind of medium sized pot. This one right here, which is so cute. And a few different sauce pans as well. This little cute one. I'm just picturing like making hot chocolate and this on the stove. So all around 90 bucks for one, two, three, four, five, six copper pots with lids. Like that's pretty good I think. I did end up getting the Fox knocker. This guy was $20, but I couldn't pass it up. I was like, you know what? There's gonna be a day where I wanna put this on the door or I wanna put it. I can even put this on my front door and literally utilize it as like a knocker. Like how cute would that be on like my antique front door? Which doesn't currently have a knocker on it. Like the little low Fox door. Here's an item that I think some of you might like and some of you might not like. This is a wall sconce. And when I saw this, immediately my mind was like, oh my gosh, Spanish revival iron. I love it so much. And I'm so happy I got this piece because you guys, it was $60, which I feel like if I was to find this at one of the antique stores or one of the architectural salvage places I've been shopping at, this probably would have been more along the lines of four to $600. I got it for 60, which I thought was a great deal. And I am picturing this just kind of mounted above a credenza, above like a side table or something and having fresh flowers, just kind of contrasting with the harsh, dark kind of gothic look of this piece and then having fresh florals, really contrasting that and kind of toning down and softening up this silhouette here. The items that I can't really pick up are one that is right here, right underneath the camera. And that is the chest. I got the Spanish chest because another item, I could not pass that up for $300. Like it has so much history. You can just tell by looking at it. He said it was from Spain that he brought it from Spain and I think it is beautiful. It is so stunning. Has the original key as well. How? Like, when do you ever come across forged key like this? So unique. I absolutely love that. And the last thing I got was from the same guy who delivered the chest this morning. Oh, and by the way, he delivered the chest and the ottoman to me, which was so nice for $40 because I couldn't fit it in my car. So he's like, oh, I'll totally deliver it, which was so kind of him. So he brought the chest this morning. I also got that ottoman, the leather one that kind of is like that burgundy toned leather. I just thought it was such a high quality piece. Like the leather is so thick and it's woven so nicely. It's just such a perfect ottoman. It's really comfortable as well. So I bought quality ottoman for $150. I would grab that. I have been talking away, but I have three more items I gotta share with you guys. One of them being these dining chairs. And these dining chairs are beautiful. I absolutely love.  The tone of wood is beautiful. I love how squared off the construction is. And look, this is one of my favorite details. The back of it actually pivots back and forth. So you can kind of like, like if you wanted to sit like this, you can lean back on the chair. I think it is so cool. It's like totally, I don't even know. I think that is so unique and the chairs themselves are just gorgeous. So I got four of these chairs, which are gonna be going in one of the breakfast notes, not too sure which one yet. Another chair I got, which is a accent chair, is right over here. It is a read chair. And I saw Brittany have this and a ton of photos on her Instagram before I even knew her. And I always was like, I will have one of those chairs one day. Like I thought it was so unique. And then as I was purchasing the item upstairs and these right here, I just asked her, I was like, is this, do you still have this? Is it for sale?  Like I was gonna keep it, but I'll sell it to you if you're interested. And so I ended up getting it. It is a dark, kind of like walnut toned read chair and it is so freaking unique. It's almost like a sculptural take on a beanbag chair. I don't know, there's something about it that is very unique. But I just got one of these because down in this lower living room, I wanna create really fun sitting areas down here and just make it more of like a very open space for people to sit, work on their computer, things like that. But the last item I got guys, which is the first piece I ended up adding because it matched my dining table, which I also got from Brittany. You guys remember that really old 1800s dining table that I have at my apartment? Well, there actually was a bookcase that matched and at the time I couldn't afford the bookcase and the table. It was just a little bit too much. So I was like, you know what? I'm gonna get the table and the bookcase was still available about six months later. So I ended up adding the bookcase, which was so exciting and it is here as well. It is gorgeous. The chunkiness of the wood edges, the shelves themselves, the finish of the wood. This was another piece that was created from wood from the 1800s. So shipping cargo crates were actually transformed into this bookcase. Oh my gosh, I almost forgot. I also got that easel, which you guys saw in the video. I got the easel as well for $35. So yes, that wraps up everything that I got quite a bit of furniture. I honestly, I don't really need to do much more furniture shopping I feel like until I move in and see what I actually am gonna be moving over from the apartment over into here to see what I'm keeping, what I'm adding, what's going to be purchased, things like that. I also will say though, I've just been having so much fun filming for you guys and going out shopping and I'm taking and looking around. Like I feel like I have a purpose to do so and I just have been loving it. So I've been purchasing way more than I probably should but I love everything I've gotten. So that's great.  I wanna thank them so much for sponsoring today's video and I hope that you guys enjoyed this one. I cannot wait to find hardware for the kitchen. I can't believe I haven't found any yet. I've only been looking for a day though. Like why do I do this to myself? I put so much pressure on myself but you know what? We're gonna have hardware in the next video just you watch. I'll see you in the next one. Bye guys.


Hi, I'd like to introduce you to this new third year program at Prague College that we can finally offer for the first time. I'm the program leader and I'm also a filmmaker with many years of experience as well as have been a student in various different film and art schools around the world and a teacher with over a decade of experience. So I'm very passionate about building a degree that is going to bring all of that experience into a practical, exciting, fun course that will actually offer you real-world experience that you can go out into the industry with. There are five really important skills that you will develop in this third year. One, be an excellent storyteller, evocatively and directly connect to your audience no matter what media. Two, work creatively and collaboratively with performers, actors and your crew. Three, develop really high-level technical skills in production and post-production. Four, have access to mentors and professionals in the industry so you know what's important to take out into the real world. And five, develop a voice that is yours, that is truly yours. What are you trying to say? What's your brand and how can you put together a portfolio of work that will say that? So if you're interested in developing those skills as well as gaining the confidence and professionalism that you will need to work in the media industry, look at the links attached, find out more about the program and I hope to see you here.


In this chapter that's titled force migration and attitudes towards domestic violence, I explored the effects of force migration in the case of Turkey on women's attitudes towards domestic violence. And let me start by giving a more general motivation. So by now we have a large body of evidence showing that civil war and conflict can have long-run consequences on various economic outcomes and human capital accumulation. But we have relatively little evidence on the long-term effects of force migration, which affects millions of people in the world today. So the most recent official data I could find was from UNHCR going back to 2015 and even then there were 60 million official displaced people and slightly more than half of them were actually internally displaced people, which we have even less evidence from. So of course now those numbers are much bigger yet we know little about the long-run effects of force migration on economic outcomes. Also we have very little evidence on effects on cultural norms and attitudes. So the question that I try to explore in this chapter is can conflict induced force migration have effects on gender norms in the long-run? And if so, through what mechanisms could these effects take place? So I explored this in the case of the force migration that resulted from the conflict between the Turkish state and Kurdish militia in the mid-1980s until the end of the 90s. And during this period, of course, the type of force migration that might result from a conflict can vary from different contexts. So I think it's important to pin down exactly what type of migration I'm studying here. So the type of migration was mainly from rural to urban areas and targeted towards the Kurdish population. So during this period, and I will give much more detail about the context in a few slides, the main impact was that Kurdish villages were destroyed for various reasons and according to official figures around a million people were displaced and mainly from going from rural to urban centers. So to study the effects of this on attitudes towards domestic violence, I used two data sources. And the main one is using two waves of the Turkish demographic health survey. And I used the two most recent waves. There are four waves. I used 2008 and 2013. And the reason I use these waves is because there I have detailed information on three dimensions that are key to my identification strategy, which is based on a triple difference in difference along these dimensions. So the first one is ethnicity. And I used the information on the mother tongue of the respondents. And the main groups are Kurdish versus Turkish in the sample. The second one, the second and third one are based on this migration module, which wasn't done in the other waves of the DHS. But in these last two waves, there is a detailed module about the migration history, which records both the place of origin, the birthplace and also the place of childhood until age 12. And then every movement for any location where the respondents spent at least six months. So that enables me to identify the time and the direction of the movement, which is important for the identification strategy that I use in the analysis. I also have a unique data set that I use more for supportive evidence due to various limitations that I'll come back to. And this data I acquired it from an NGO, one of the largest NGOs in the country, that aims at helping women that suffer from domestic violence. Due to anonymity reasons, I'm not going to name that, but it's a large NGO and the data covers essentially the universe of applications that they received from 2009 and 2011. So of course, this has lots of limitations. That's why I'm only going to claim that it's some supportive evidence. But I think it's also important to look at it and I'll come back to that later. So, of course, the first question is, why should forced migration affect women's attitudes towards domestic violence? And I think Roberto did a great job in summarizing the literature on intrasol bargaining. And one potential mechanism might be related to that. And in a standard cooperative bargaining model, we would expect that if for any reason migration improved women's outside option, then that should lead to a drop in the rate of domestic violence and potentially longer and also about expectations and acceptability of domestic violence by both women and men. But in the data, I only have women. That's why I keep preparing to women's attitudes towards it. But on the other hand, we know there's a growing body of literature as Roberto summarized that non-corporative bargaining models seem to be, at least in the developing countries, settings more and more relevant. And in fact, if we model domestic violence under such a non-corporative bargaining situation, it's not so clear that an increasing women's outside option will necessarily lead to a reduction in domestic violence. And in fact, literature outside of economics and sociology has for a long time written about the male backlash theory that if women's autonomy improves, this might create a backlash from the men and that might result in more domestic violence. So theoretically, it's not clear which way the effect might go, but to the extent that if forced migration have had an impact on women's outside option relative to men, then it is likely that it might have had an impact on domestic violence and the attitude about domestic violence. So as I mentioned before, going back to the Turkish context, the main movement was from forcing populations to move from rural to urban centers. And both in my data and in general in other studies that have looked at female and male labor force participation rates, it seems that in Turkey, the stylized facts is that in rural areas, female labor force participation and employment rates are relatively higher compared to urban areas, and especially this is the case for women with low schooling. On the other hand, for men, we see a smaller difference between the rural and urban, but if anything, the difference is the other way around. So in the urban areas, male labor force participation rates and employment rates are higher. So unemployment rates for women have been shown to be higher again in the urban sector and especially for unskilled labor. So in that sense, it is not unreasonable to expect that this forced migration may have reduced women's employment opportunities once they were forced to move to the urban centers and most of them either don't have any formal schooling or have very little. So they were definitely in the unskilled labor. So if we go by just the simple rates of employment rates in rural urban areas for low-skilled labor, it is likely that women's employment rates were lower and in fact I will test and show some evidence in line with that in my dataset as well. So in that sense, it seems like the bargaining power mechanism might be active and that could be one potential channel. Another channel might be through cultural diffusion. So in Turkey, if we again look at the dataset, for example, the DHS data, we see that in urban areas on average respondents are less likely to say that domestic violence is acceptable compared to rural areas. So in some sense, in urban areas, on average, there is more, we can say there's more progressive gender norms about domestic violence. So in that sense, since this movement forced populations to go from villages to the urban centers, it might have led to a cultural diffusion whereby the migrants' norms eventually change in the long run and might become also more not accepting towards domestic violence. So this would go in a positive way, making domestic violence less acceptable. So what I find in the data is that I find that Kurdish women who migrated during the conflict from the conflict region, who were from the conflict region and migrated during the conflict, they are 10 to 15 years later when they are surveyed, they are significantly more likely to say that they think domestic violence is acceptable under at least one of the situations they are asked about. And the magnitude of the effect is really large. So it's around 50% relative to the group I use as the counterfactual in the difference in difference analysis. So it's a big increase in their rate of finding, reporting that domestic violence is acceptable. And when I try to look at the mechanisms, I find some evidence in line with the bargaining power mechanism. In particular, I find that forced migrant women using the same identification strategy, they are more likely to be in a relationship where their spouse is working but they are not working. So the gap in the employment rates between men and women in these households has increased and this is again in the very long run. So unfortunately I can't look at the immediate effect when the migration took place but I can only look at it at the time of the survey and it seems like there is still some significant difference between men and women's employment rates. And also there's more poverty still so they are on average below the middle class in terms of the wealth score that DHS assigns to households. So both the poverty has increased and also the women's bargaining position relative to men seems to have decreased. And I use the second dataset from the shelter NGO. So this dataset has, as I said, various limitations and the main one is that it's of course a selected sample of women who decided to apply to this shelter. So they decided to seek help for domestic violence. The advantage of the data is that it is first of all reported by the applicant to the center. So in terms of the reporting bias that we typically expect to see in domestic violence cases even in criminal records, it's relatively reasonable to think that what they report there is quite truthful and there's relatively detailed information about the case, the type of the violence and the duration of the relationship in which they were subjected to it. And when I compare, controlling for as many things as I have information on, the forced migrants to the rest of the applicants, I find that forced migrants have been in the relationship for a longer period before applying to the NGO. And they are also less likely to have sought other types of help compared to the others. And they also seem to suffer more extensive violence. So on the intensive margin, they have suffered both for a longer period and also more severely before deciding to come to the organization. So that, again, as I said, is suggestive. There are various shortcomings that we can discuss, but it supports the idea that the forced migration may have reduced their likelihood to not find domestic violence acceptable and to seek help quickly. So overall, both findings are in line with that. So in terms of the related literature, first of all, it's related to a large and growing literature on the interrelation between conflict, civil war and conflict, and domestic violence. And here there's little but growing literature that tries to estimate these effects. I think in particular, one paper by Julia Lamatina that was recently published in the Journal of Development Economics is quite closely related. And there she looks at the effects of the Rwandan genocide, the longer effects on, in terms of domestic violence rates. And she finds similar evidence. But there the mechanism is very different. She looks at the big effect on the sex ratios in the population and finds that where women's sex ratios have fallen down, the domestic violence rates have increased more, which seems to suggest that the bargaining power through the marriage market is more relevant. So in the case that I'm studying, the direct effect on sex ratios is likely to be not so large. The number of deaths was nowhere as large as in the case of Rwanda. But other, the bargaining power effect might again be there through the employment mechanism that I just described. And then it's also related to a larger literature that looks at the interconnection between women's bargaining power and domestic violence. So on the empirical side, as Roberto also mentioned, the evidence is like if we look at it globally, it's mixed. But if we try to make a general conclusion, we can, I think, say fairly that in developed countries or high income settings, typically when women's outside option improves, domestic violence seems to go down, whereas in low-income countries, it's the other way around. So Turkey is, of course, kind of in the middle. And in that sense, I think the two papers I mentioned here by Manuela Angelucci and Bobonis Atal from Mexico are probably like if we just go by rough GDP per capita rates are the most comparable. And there they find, on average, that an improvement in women's bargaining power, which they are exploiting the conditional cash transfer program that was targeted to the women in the household, on average, is associated with a fall in domestic violence. But the effect is very heterogeneous. And depending on the size of the transfer, Manuela Angelucci shows that it might be the other way around if the transfer is very large. But on average, it's in line with the thing, the results I'm finding also in the Turkish case. Of course, I'm not looking at the rate of domestic violence, but if we can think that the two are correlated, then it's similar. Sorry, here's mine. Finally, I think it's also related to a growing literature on the connection between the effects of migration and cultural diffusion of cultural norms. So there, of course, this is difficult to estimate the effects of migration on the migrant's attitudes, because migration is typically very selected and endogenously so. So migrants, people who decide to migrate presumably do so, also taking into account the existing cultural norms in the places they choose to go. But in the case of forced migration, I think this selection effect is less so, or if anything, it's not there at all. So in that sense, exploiting forced migration might be kind of a methodologically convenient way to also estimate the effects of it on the migrants' attitudes. Okay, so for the rest, I'm going to first give you a bit more context about the migration that I'm studying here, and then talk about the data results, so yeah, pretty standard. Okay, so in terms of the context, so the conflict here was mainly between the Turkish army and the state and the Kurdistan Workers' Party, or PKK. And even though PKK was founded before towards the end of the 70s, the violence really escalated in the mid-80s, and from 84 until 99, it continued at a very high intensity level. And then it kind of died down with the capture of Öcalan, Abdullah Öcalan, the leader of PKK during that period. But then, as we know recently, it's escalated again, but I'm not studying the recent wave. I'm looking at this period, and during this period, there were many villages that were either evacuated or destroyed. And according to the official parliamentary report from 98, this report cites three main reasons. The order that I listed them here is the order that's in the report, and I'm not sure if that order is correlated with the relevance of that reason by any means. But the three reasons mentioned in the report is one is the collapse of agriculture and animal husbandry, which are the main economic sources of income for the livelihoods there. The second one is that they're saying that PKK was evicting villagers and destroying villages that were cooperating with the state. And the state was also evicting villages that were not cooperating with the state or cooperating with the PKK. So which one of these was the main driver? As I said, there's no official figure on that, so I don't wanna make a judgment personally, but all three of these effects were there. And in the end, again, the official figure is there was between 900,000 and 1.2 million people that were displaced because of that. And again, recent information shows that even as recent as 2009, only 20% of them had returned back to their villages. So in terms of the location, so this is the map of Turkey and the provinces that are highlighted green is the area where the high intensity conflict was taking place, although there were also terrorist attacks and other types of maybe more minor conflicts in other parts, especially in the surrounding provinces. But these states were really where the conflict was happening and the most of the migration was also taking place. They were also declared by the government to be under a state of emergency all throughout this 8499 period, which gave the government more rights. And so the evacuations were really taking place there. So I'm going to compare essentially people from this area and migrated from this area with people from the other provinces. So the blue ones are the ones where, again, according to the official report, most of the migrants went and settled. But also in this area, as I said from the villages, a lot of them settled in the town, in the province centers. So the data that I'm using is coming from the DHS. And as I said, the main advantage here is that there was the migration module that enables me to do the analysis that I'm conducting. The respondents were, for the last way, all women age 15 to 50. And as many of you know, I'm sure the DHS surveys that age group because of reproductive modules, etc., are mainly targeted to that age group. And then in the 2008 wave, they only surveyed ever married women. I'm going to pull both data sources mainly because I need a lot of power to do the triple difference analysis. But I control for, of course, survey wave effects and also the marital status to make sure that the results are not driven by this restriction. And for my main outcome is I'm using this module on the respondents' attitudes towards domestic violence, which exists in most DHS waves. Some DHSs also ask about direct incidents of domestic violence. Unfortunately, in Turkey, this was not included. So I only have the attitudes towards domestic violence. And here, there were seven different situations that the respondents were asked about in 2008. And then in 2013, they limited that to the first five. Again, I'm going to use both, but the results are not really driven by the last two. So if I exclude those two and only focus on the five, I find similar effects. And the situations, I mean, I don't think they are directly important, but it's things like if she burns the food, if she neglects the children, etc. But I use all of these things to build indices in three different ways. The first index will be whether the respondents thought that domestic violence was acceptable under any scenario. And the second will be the fraction of scenarios. And the third will be a principal component. But I think it's fair to say that the first index, that it shouldn't be accepted under any scenario. It's probably the one that is most relevant. And finally, the ethnicity is the third important dimension that I look at. So these are just some descriptive statistics from the entire sample. And then if I limit the sample to people that grew up in these conflict provinces, and you can see that the sample goes down a lot. And then if I also look at the ethnic division within the sample, people from the conflict region, a larger share of them are Kurdish because that's where the Kurdish population is mainly based. And in the other regions, there are way fewer Kurdish respondents. And let me see. So in terms of the main outcome of interest, on average 20% of respondents reported that they think domestic violence is accepted under at least one of these situations. And you can see that the rate is higher for respondents from the conflict region. And especially when we look at people that migrated during, it's even higher. It's 38%. Of course, that could be due to lots of things. So it's important to control for as many predetermined covariates as I can. So I'm going to control for things like their parental education, whether their parents were related. So basically anything that I have that are likely to be predetermined to the migration, I will control for in the specification. And so the main specification that I estimate is, as I mentioned, is a triple difference and difference model, where the differences come from the ethnicity of the respondent, whether she grew up in the conflict region, and then if the migration happened between during this conflict period. Now, of course, one caveat is that it doesn't necessarily mean that it's a forced migration if it happened during the conflict period. So here, unfortunately, I don't have exact information on whether it's forced. It was not asked, but I'm assuming that people that migrated during this period are more likely to be forced. So in a way, you can think of it as like an intention to treat thing, but I don't have the means to only focus on forced migrations because I don't have information on that. I will control for respondents age and age squared, which was significantly different between the people that are migrating during this period versus not. So that's, again, because I'm looking at the effects from a survey 10 years later, of course, people that moved during that period are older. So I'm controlling for their age and age squared, their parents' education, et cetera. Right. So the parameter of interest will be the triple interaction between these three dimensions. So in the paper, I report all the controls, but here in the interest of you being able to view it, I only show you the triple interaction term. And you can see that. So here in this column, the outcome variable is whether respondent things, domestic violence is justified under any scenario. The point estimate is 16 percentage points. If we compare it to the mean in the full sample, it's a very large effect. So the mean was 20%, but perhaps the more relevant comparison group is Kurdish women who did not migrate during this period but are from this area. So for that sub-sample, the average level of the outcome is 32%, which still means that this is a 50% higher likelihood that they think, so Kurdish women who migrated during the period are 50% more likely to think that domestic violence is justified relative to Kurdish women who did not migrate during that period but are from the same area. Right. And then if I use like the other indices, this fraction of scenarios that she thinks domestic violence is justified or the first principle component, the percentage effects are similar relative to the mean. So I try to do a few types of placebo tests in the paper. Actually, this wasn't in the draft that I think was circulated, but it will be in the next round because these are more recent. So in particular, I tried to use the small sample that I had of people that moved before the forced migration and then compare people that moved before the conflict period and people that moved way before the conflict period. So kind of a placebo test and there I don't find any significant effect. So that kind of is reassuring I think because it shows that the previous effect that I showed you is not driven by some preexisting trends that was already taking place, which is in a way supporting my identifying assumption of parallel trends. If I look at the breakdown of the different scenarios, again as I said before I'm not sure exactly how to interpret them but it seems like the effect is more precisely estimated for these three scenarios about neglecting children's needs, arguing with their husbands and refusing to have sex with them. For the other scenarios, the coefficients are positive but usually imprecisely estimated, but they're all imprecisely estimated. But as I said, I think it's fair to say that all of them are positive and for most of them it's similar. Okay, I have 15 minutes. Now in terms of the mechanisms, as I said before, it is reasonable to expect that the migration may have reduced women's employment opportunities. So if I look in the overall sample at employment rates of women, I also see what previous literature from Turkey has shown, which is that women in urban areas their employment rate is lower on average compared to rural areas in Turkey. So in rural areas women's employment rate in my DHS sample is about 40%. In the urban areas it's 29%. And of course if we break this down by schooling level it's very interrelated and women that have low schooling their employment rates are way lower in the urban area relative to the rural whereas women that have high schooling there's no significant difference. And then for men, even though the differences are smaller, the reverse relationship is there. So on average men are more likely to be employed in urban sector but in the rural sector they are less likely to be employed. So I'm not trying to say anything about why this is, it's beyond my paper although it's interesting. But it's reasonable to expect that this forced migration may have reversed or affected the bargaining position within the household. So for that I look at the likelihood that their husband is working when they respond and it's not. I see that there's an 11 percentage point increase in this probability even though this effect is 10 to 15 years after the migration and it's a large effect relative to the sample mean. So that again is in line with the bargaining power being affected. Okay now in the remaining time let me also show you what I do with the data from the applicants to the women's shelters. So as I said this is one of the largest NGOs in the country and it is well known and so even though I don't try to claim that this is the universe of women that seek help, it is a large share of them. And I have around 3,000 applicants that came to the NGO to seek help between these two and a half years. It's from mid 2009 until the end of 2012 and I use this to test if forced migrants have remained in an abusive relationship for longer and also have suffered like more intensive domestic violence before at the time of the application. So these are some summary statistics from this data set which is really like not the most uplifting thing to show unfortunately but so the type of information on domestic violence are things like so I have one question about the duration which is if whether the relationship in which domestic violence was experienced started more than 10 years ago and also whether the domestic violence started at the beginning of the relationship. So I combine these two things to define a variable which is violence has been going on for 10 plus years and 30% of the applicants said yes to that and if I compare forced migrants to other applicants it's much higher amongst the forced migrants and the way I identify forced migrants here is different there is no migration history but there is a direct question have you ever migrated and then if yes what was the reason and one of the options is due to security reasons and discussing the way the data was recorded with the NGO workers that means it was forced and so I define forced migrants based on that. And then other outcomes is like whether she received any treatment physical or psychological treatment before coming to the center only 12% of the applicants said yes whether she filed a legal complaint very small only 4% said so and it was much lower again significantly lower amongst the forced migrants if she complained to anyone including friends, family or authorities only 52% of the applicants said yes to any of the potential places and again the rate is lower amongst forced migrants half of them were raped and again the rate is much higher amongst the forced migrants and finally there was one question about if she had a miscarriage due to domestic violence 8% of the applicants said yes and the rate was almost double amongst forced migrants to other applicants so in the analysis I control for I regressed these outcomes on being a forced migrant controlling for being a migrant in general I also control for additional things that I have information on such as the timing of marriage whether the marriage was forced marriage if it was a family marriage with a cousin or a family member whether the respondent has an independent source of income so even things that are potentially endogenous to forced migration I control for to see if these are driving them and it seems like with all of these the correlations are robust to controlling for so forced migration seems to be positively correlated with the likelihood of having been in the relationship for more than 10 years before coming to the NGO negatively correlated with receiving other treatment and negatively correlated with seeking other help which is kind of important because of course it could be that maybe they didn't come to the NGO but they went elsewhere so that doesn't seem, unfortunately that doesn't seem to be the case there is no significant at least correlation if anything it's negative between going to the police or friends or family being a forced marriage finally when I look at if they were forced to have sex against their will or if they had a miscarriage they are more likely to say yes to those as well the forced marriage and as I said the relationships are robust to controlling for the things I have information on so to conclude it seems like the conflict induced migration in this context led to an increase in the likelihood that women think that domestic violence is acceptable and this was a long-term effect 10 to 15 years later and one possible mechanism that I'm able to kind of test for is the bargaining power effect and I find evidence in line with this that women's bargaining power women's economic opportunities and therefore their bargaining power was reduced relative to men because of the forced migration from rural to urban areas in this context of course this doesn't mean that it will be it will work in the same way for all types of forced migration it depends a lot on the context and where they are forced to move from and to where but in this context this is what happened but overall I think we can draw a general conclusion that it is reasonable to expect that forced migration might lead to effects on gender norms and it might have bigger consequences in terms of women's well-being so the effects might be heterogeneous by gender and I think it's important to take this into account when designing and implementing policies on forced migration thank you