Thanks everybody for coming in here today. I know that we're reaching the end of our first day and people are quite tired Maybe a little bit jet lag, so I appreciate anybody here who's coming in is interested So a little bit about me. My name is Lisa I am a product manager at data straight-o quite newly minted in this role, but really enjoying it I previously spent a few years as a data engineer and as a data analyst before and I've been through a few different industries Mostly startups and mid-size companies and it's been really fun to see kind of the different data architectures and the different approaches to Data ops. I'm also a Google woman technic years ambassador, and I've been part of the community for a little while now So a little bit about what I hope that you'll leave this talk with Really, this is a huge talk in terms of the scope that we're looking at things And I'm gonna kind of give you a quick warning here that we're gonna cover a lot of topics But we're not really going to get into the meat of anything and that might be a little bit unsatisfying But that's sort of the point of this talk that you get this true end-to-end perspective of data ops and em all ops and You know kind of the different aspects around that few of us get to really touch all of these different things and I'm gonna talk about and so, you know, how can we gain that sort of perspective and Really, I also want to provide some relevant frameworks, especially within the open-source space all the technologies I'm talking about today will be exclusively open-source and Where you can kind of concentrate your efforts. I think that teams can be stretched really thin especially when it comes to Data ops in general and so we want to really make sure that our efforts and our energy is being put in the right places I also want to go over some other general definitions So data ops, I think can be a bit of a buzzword. I think a lot of people don't necessarily know the consensus of this term I'm not going to claim that this is the definition that the entire field agrees upon but for the purposes of this talk I really want to consider data ops as all of your data Automation and operation needs especially from a dev ops perspective with regards to your service systems and pipelines and kind of Securing those as well. If there was anything like a data supply chain this is kind of where I would want to to be tempted to use this terminology and MLOps as being this end state of it where you know it I don't want to consider it as a tag-on But I do think that MLOps is so specific that it really you know deserves it its own sort of culture around it And so that's kind of where I'm going to be working in terms of definitions here So some of the data ops components that I'm going to be discussing is going to be the orchestration of an abstraction of your pipelines How you can implement some governance and data contracts? Really looking at metadata management as well and using all of these tools to for the benefit of your data quality for your monitoring and Observability and how we can bring in these sort of core DevOps concepts into your data platforms and data teams such as you know CICD Containerization and deployment and documentation so again This is a lot of stuff like this is a huge amount of ground to cover But I think it's really useful for us to be able to look at it all So some of the increased needs for data ops that have occurred over the few last few years I think the big push has been us moving into data platform and data mesh really moving into non-linear ways for us to serve data and Trying to bring in reliability into these systems So of course migration into cloud data systems has been really important for us And I think a lot of data teams work almost natively in the cloud now and maybe hybrid on-prem as well But it's quite rare to see like just solely on-prem and with that comes a lot of considerations There's increased complexity when it comes to our data pipelines and data systems as well Just in terms of the number of sources that we have to manage the number of formats What we process for and now that we have machine learning kind of picking well machine learning in full-flex How we manage data quality specifically for machine learning as well I think that data teams have become siloed and spread out so instead of maybe an organization having one data team You might have an embedded data analyst on each team And how can we make sure that all of these analysts or or machine learning scientists are equally supported among their Organizations and especially how can we make sure that they're getting the the best data quality possible without having to Constantly feel like they have to feed back into another team So again talking a little bit about all of these different architectures that we're seeing now and how can we best support them Some of the benefits that we're looking at for data ops. I don't think that I need to sell this too hard But I think visibility is really at its core. How can we track our data lineage? How can we introduce observability into our systems? How can we assess data quality at this really high level when you're dealing with such gigantic data systems? Again practicing data governance is really important these days Especially when it comes to legal compliance and security and we also want to make sure that the uptime for our data and you know The guaranteed availability of our data is something to really consider How can you also streamline so much of our data infrastructure and architecture and how can we abstract that out in a way That makes sense, but doesn't oversimplify things We want to again in introduce these core software engineering principles as well such as versioning iterative development and Configurability into our work and a lot of this might seem really Like intuitive for us But if you've been part of a data team like a team with just data scientists and data analysts You'll know that these skills have to be learned and it's something that it's really intentional So Implementation challenges when it comes to data ops I'm gonna be honest the biggest one here Especially in most organizations is going to be the stakeholder buy-in all of this is infrastructure all of this is dead all of this is stuff that needs to be continuously maintained and So I think getting buy-in if you're a data driven organization And if machine learning is maybe the core of your product or data is the core of your product You might get a little bit of an easier time really diverting resources into this sort of stuff but generally speaking if you're like 90% of the other companies out there This is something that you kind of have to to finagle your way into so again thinking about interoperability between our different technologies How do we standardize the formats and our practices as well? How do we avoid vendor lock-in which in terms of scalability can be very costly down the line and all of these different things that really make us You know kind of wary to adopt new technologies and adopt new practices And we might think of this process as very straightforward But it's really quite linear because we want to go back and improve processes As these holes pop up and we're not going to do it perfectly each time around So I'd like to propose for different strategy domains for companies to focus on and for organizations to focus on and They're not all equal But I think that the pressure and the ways that we run into these things tend to to rise over others depending on what? Your business goals are I think a lot of the push for data ops and increasing quality Really comes down to like hey, there's something wrong with the numbers let's go back and see where it goes and that's going to be our accuracy and data quality and How we're affecting our downstream reporting and metrics whether or not our models are outputting the things that we want them to and how they're performing and This is kind of going to be your biggest sort of push initially And this is because all of your stakeholder trust is going to come from this and this is kind of the thing that affects the core business the most Next I want to go into visibility and transparency So once you have a complex enough system having data lineage and being able to track the different stages of your data And its source systems is really important This can also be in the form of metadata collection observability documentation and just Overall making sure that this isn't just this black hole that this data is coming out of and we don't necessarily know where it's it's coming from right and When something changes we want to be able to track that over time Reliability and latency is going to be another domain that we're really going to have to look at and At certain scale you're going to want to introduce some sort of monitoring into your data So are these values looking you know how we want them to is their giant holes in the data That's coming in and how do we optimize our data systems to to be less costly? And I think FinOps is a really good application of this as well How do we introduce fault tolerance into our systems and just generally really good principles? Security is going to be our final domain not that this isn't extremely important but I think if you're coming from a data background this does tend to fall by the wayside and so it's something that needs to be very intentionally brought in and discussed and And again, I'm going to go over this data pipeline but I think that a lot of people here don't necessarily need to know what an ETL pipeline looks like and Generally the the whole point of me bringing this up is to really show that There's different hands on all of these stages. There's different people who touch and are in charge of processing and moving the data and at every single one we kind of need to consider the underlying currents that might Play into that. So we might think of just building this pipeline as you know our one and done Okay, it's there. It's up and it's moving But really we want to make sure that it's maintainable over time that we can have iteration over it and that we can have visibility and control over different aspects And so this is really what data ops is meant to to be and this is why I consider it to be a little bit different from Normal DevOps and why I also consider it to be different from ML ops, right? So some of the tooling that you'll see around this There's a ton of tools around data pipelines and we'll get into data ops tooling later But I just want to kind of have this slide to point out that a lot of the open source concentration has been around just manipulating data and Actually, and I think that makes sense considering where we are But really we want to consider the the cost optimization undercurrents of this and what our actual business requirements are and these sort of drivers for it But really again bringing it back to data quality When we go back to This particular image It's very straightforward and oversimplified here, but at every single one of these stages Your data can get wonky like anytime somebody touches something Anytime there's a process or a step or some sort of code in place you're kind of putting your data at risk and so data quality issues can be unavoidable especially at scale when you're dealing with so many complex pipelines and not all the metrics that we measure data quality by are going to be equal and More so importantly, it's not going to be worth your time to pursue That's something that's really important to take note of is that when we talk about ops everything's this nice to have and nobody If you pitch it to somebody they're going to be like yeah, that sounds great But then you ask them for the resources in the time and the money and they're like, oh, maybe not You know we have better more important things to do So the way that we pitch a lot of this within an organization is going to be centered around our business needs again, we want to gain that consumer trust and we want to make sure that we have trust within our own data systems and One of the the early data engineering examples of this is the right audit publish pattern that You can use using many different open-source tools on the market And I think that's a really great example for how we can really Like put in these different processes innately within our systems and pipelines So when we talk about data quality, there's really like these five pillars of data observability You've probably seen these before but how do we actually measure the shape of our data? And how do we make sure that what's coming in is what we're looking at and over time? How do we track these things? These are sort of key metrics for observability if you're going to track something over time It might as well be like these five but again, they're not all equal depending on the data that you're working with so Bubbles are different sizes, but really it depends on your organization So the first is going to be you know freshness is this data recent Is there a hole in when our data is being pulled and is it up to date? How is the distribution of this data? Is it within the values that we're expecting and this requires a bit of domain knowledge and it depends on what your data Actually is right and it requires a sort of cross-functional aspect to this as well am I working with the analysts who actually understand what this data is supposed to look like as a data engineer and Can they tell me whether or not these ranges are you know looking okay now? Or will they tell me six months down the line before reports do and it's already kind of broken That's a really disturbing thing to have to deal with us minute Can I track the lineage of my data this becomes more important as we have these iterative? Models that are coming out and we want to make sure that we can track what our data is that we're feeding into there and make that really easy Is the schema of our data actually you know stable Is there some sort of did somebody push an engineering model that wasn't cat caught and Is that going to change something and how can we track that? I think data contracts are a really great way to tackle this particular issue and of course the volume of the data as well Which I think is our first instinct when we're really trying to do a sequel check against our incoming databases so there's a couple different tools that we have around this as well and so really we want to Give heed that and I'm not saying that you need to use all of these And I don't think you should because the technology is probably not relevant to your stack depending on what you're running on but Data quality because it can break at every single stage of your pipeline You want to make sure that you're checking your data quality at every single stage as well That's kind of unrealistic, but at least at ingestion maybe at the source system level and Right when it comes to an analyst, right? So I think great expectations is a great example of a tool that a data scientist can use To test a data frame that's already been modeled and then early on you might want to use dbt If you have an analogous engineering team and do some unit testing there as well And how can we unit test our data at various stages and to make sure that the data is being shaped how we want it to? I also added a patchy airflow here as well It's not necessarily supposed to be used this way But there's a couple of different ways that when we're bringing in our orchestration tools that we can you know Break the circuits when something's not looking right or add like sequel check operators And it depends on the cloud environment that you're working on as well Metadata can also be a really powerful tool moving To this bigger plane from just data quality We want to be able to have a really good understanding of our data without having to invest in all of this infrastructure just to test Really hard-coded things and so to have dynamic and active metadata can really enable so much more in terms of your infrastructure So some examples of this can be descriptive and structural and administrative context to your data that you might be able to have and have easily as well It's useful for data discovery, you know lineage and generally being able to virtualize your data can be very powerful Especially if you're on something like a data platform team. There's a couple really cool tools out there for this Apache iceberg. I think is really popular these days. There's also months in data hub And Gravitino, which is where I'm coming from today so the company I work with is called data straight-o and I do product with them and we're open sourcing our Metadata Lake today, which is called Gravitino and we have a little QR code if you want to be taken directly to our GitHub repo and so we're still actively working on this and Essentially what we're trying to do is create metadata virtualization layers over top of your existing Databases, so if you're say working in a multi-cloud environment where you have GCP and AWS or you know Azure and AWS How can we make sure that that metadata is consistent across all systems and you can kind of have this really technology agnostic way of Accessing all of the different pieces of data in your system without having to do really complex joins across from them and Some sometimes that's impossible as well So what we're really trying to do here is create a single source of truth for all of your data needs and Really create that layer where people can have a lot of trust in So to kind of review over this framework that I've brought in There's a few different domains, but there's also a few different places that you want to concentrate on again basic version control it seems really Intuitive for a lot of us who come from programming backgrounds But having all of these principles really heavily ingrained within your machine learning engineers and data scientists is really is really important As well as bringing in specialized tools to version your data So including like maybe DVC or debesium if you're really into Trying to version that actual database as well as implementing data contracts similar to API contracts Where you want to make sure that your schemas are enforced in some sort of way as they're being ingested? Metadata can be really important for having other applications be able to query and to be able to create images of or Create really a shape of what your data is and to gain a lot of information without having to do all of this heavy processing and we're trying to do that at Gravitino and We also want to be able to monitor and create alerting systems for when our data systems are down and to deploy them and to document them as needed as well So another aspect of data ops that I want to discuss is also going to be data pipeline management through incident Management and I think that a lot of folks from DevOps backgrounds Understands maybe this idea of being on call or doing a root cause analysis But how can we also implement those sort of principles into? Data as well where we're understanding that if a data system is down or if a pipeline is broken Who is going to own that and who is going to be the person on call to fix that a lot of this ownership is really just Documentation, but it's really important for us to again take that responsibility and to understand how that can benefit our organization down the line We also want to make sure that we have Processes for doing root cause analysis into our data. Do we have standardized processes where if our numbers are wonky? Who's going to do what and what steps are going are they going to take to do this? I also want to discuss MLOps a little bit I think MLOps has been a really big part of this session and this talk So I'm not going to claim that my MLOps portion is going to be any better In fact, you'll probably get a little bit more detail But I want to give an overview of what that might look like because we've discussed Pipelines so much up to this point. So really we're looking at the testing validation and reporting of These different systems and we want to make sure that our models are deployed safely But they're deployed in a way that we can manage them really effectively using large-scale infrastructures We also want to make sure that all of these different experiments that we're running have metadata that can tell us what was the the version of this validation set or Testing set that was being run with it. What's this version of the model, etc? And we want to be able to detect drift over time as well as you know These models are deployed over years and years and years and how can we then automate a lot of these processes? so Kind of a not super complicated graph But just an image to kind of show you know what might be the components around this and how we might look at it And it differs a lot from maybe the first image that I showed earlier And that's kind of why I consider them to be so different in terms of data ops and MLOps So our roadmap here is going to be pretty straightforward in terms of we just want to version control everything as a first step Especially for a very experimental and trying to just get things to work How can we also use get tags and get ops to make sure that we're able to backtrace in the lineage of our data and the models Themselves and how can we include you know this continuous deployment in there when we're deploying on to you know massive clusters And how can we package this in a way that's reproducible and nice especially if we're looking at deploying on devices natively And how can we then monitor that in a way that's scalable and manageable and of course? I think documentation is really important especially documentation for non data folks There's a lot of people who would benefit in your organization from just having layman terms Documentation around the data system, and I think that's really important to to emphasize as well So again some of the frameworks that we might want to go into here is going to be what we use to develop our models again These are pretty standard tools that I think most people here will be very familiar with So I won't go into things too much But we want to test at this end state as well as we're developing the models How do we test the models and cells? How do we test the data? How can we then serve this in a way that we feel confident in and How can we then build out the tools around that that makes sense to us and that fit our needs? And so we also want to make sure that the long-term effects of this are going to be something that we track And so a lot of this lives within the Python ecosystem currently Whereas I think in data ops you're having so many different tools that span a lot of different languages as well And I think that's where a lot of the trickiness rely resides so yeah we also have another talk if you're interested in learning about Gravitino and That's going to be an L20d hosted by Jinping I'll also be talking there as well if you're interested. That's right after this session and We're also hiring so if you're interested in working with us and you're interested in metadata Please do let me know or visit our website here and thank you so much Do we have time for questions or yeah any questions at all or well, thank you very much


Morning everybody We're at the yard Just south of Winnipeg, Manitoba, and I got some curable trucker handbomb Feel the difference from bullsnot. I think but a couple of these from bullsnot to try right? It's curable made specifically for truckers This curable trucker handbomb is a moisturizer that heals, relieves and repairs extremely dry cracked hands Curable is a trucker tested and approved or is trucker tested and approved guaranteed relief for your money back And it actually works. I actually use it at least once a day once or once or twice every day now Just in the winter time your hands really dry out in this cold dry weather The good people at bullsnot sent me a bit to try and now I'm actually hooked Get it at bullsnot.com or bullsnot Canada pick up a load and bring it to the Paw Manitoba It's about a six to seven hour drive from here once I'm loaded and then we come back. I Have an empty step deck 53 foot behind me. We're gonna go fill it up with stuff. I'm just 31 overnight Old blue kept me nice and warm. We have the weasel with us this trip We tried his new thunder shirt yesterday Which is supposed to relieve his anxiety when he's in the truck because he's getting older Mighty issues in the truck and that's why he hasn't been in the truck with me as much as well as You know, he's at my retirement. He's spending home at a time at home with his family, too But so far I mean I haven't given it a good good trial yet But so far yesterday the thunder shirt really seemed to help ourselves into traffic here rush hour is going that way Because it's all going into Winnipeg So that was the easy part. Well, it looks like we've got a good break in traffic coming up here. I Like that they made these that they made this divided highway This is highway 59 south south of Winnipeg But they made it like wide enough that I could fit my whole truck and trailer between lanes of traffic So that when I'm waiting for this side I'm not blocking the other side. You know what I mean? That makes it a lot easier to cross Flashers going. Oh, there's some over there. You know what that means though, right? You've been watching my videos long enough your sky means it's gonna get cold tonight and tomorrow Tonight the temperature is gonna drop down to about minus 30 and The next night is gonna drop down to minus 34. I'm talking Celsius 34 at night So it looks like it's just gonna be this week and then it's gonna warm up next week We've got three days of pretty pretty cold weather and you know, we're actually doing pretty good this winter I'm actually pretty happy with how this winter's gone. It's been pretty mild Last winter in the winter before it seemed like four to six weeks of just brutal deep freeze And this is our first week so far. We're already in February 2023 so we only got two more weeks left until You know, we're close enough to the spring equinox that we don't have to worry about it Slamming us down to minus 35 at night, right? It's usually the last two weeks of January and the first two weeks of February that are the very coldest after that We're usually pretty good Anytime before that is or after that is just snow a lot of snow That's one good thing about being so cold as it doesn't snow as much but perfect life You gotta have one or the other either. It's really cold or it's snowing Diesel how's that thunder shirt?  Good job Good for you, bud. It's really calming them down so far. It's working good Tried for a couple of days and see how it goes But I took it off for a little bit this morning and he practically begged me to put it back on He kept staring at me and staring at me until finally I stopped and I went Oh, I said do you want your thunder shirt? And he got started to get all excited and I pulled it out of the box in the back And he comes jumping into the back in the bed and sits there in front of me wagging his tail Got poking it with his nose. He wanted to wear it So he really likes it Right to make you feel calm diesel Super calm man show calm show calm man So the receiver where we're headed to closes at five and guess what our ETA is It is five also So as I get closer, I'm gonna give them a call See if they want to wait around to unload me today yet, which would be fantastic But if I can't convince them of that we'll have to wait in the pod all the morning And they'll unload us then not a big deal, but you know, I always like to Get as far as I can in the day I've got plenty hours like if I unload I can drive quite a ways back If not almost all the way back. I will see what happens. We'll do what happens either way I'm okay with it. It's a good day, right diesel My thunder we do Yeah, can I get a cape? I want a cape. I'm a thunder we do super. He'll make Because how good for you diesel. I'm glad you're so happy It's good to see I think this might be the solution to have him in the truck more often Not all the time but more often That is a whole house said it before but Amazon Prime delivery is getting out of control Look at this. It's huge How wide would you say that is it's wider than the entire highway 40 feet wide Holds and traffic has to go all the way like almost into the ditch. They're all confused What you thought we built them on site nowadays. It's 2023 We'll build it wherever we want to and we'll bring it to you This is actually what we were thinking of doing for our build in our land sort of changed our mind on it already but Go any further to the right because of those posts, right and all the traffic coming at them has to Just barely squeak past Those daresay. This is a little too wide for this highway. This is highway six. It's a major provincial highway It's the only highway that takes us up into northern Manitoba and all the northern communities It's a little two-lane highway like this. I would say I don't know if I was the one issuing the permits I don't think I would issue a permit for this This is a little bit out of control In this town so maybe the next town white Jeep here just had to hit the ditch There's tracks going off the road off the gravel there not too far But I wonder what the the widest Load is like how big of a house? Can you move here? How big do the permits allow them to go?  Like I said, they're doing a good job. They're not hitting anything But issue a permit for that He's got to go into the middle of the road to avoid all these road signs on the right Oncoming traffic literally has to stop way out there in a turning lane off the road.  It's too big for a two-lane road. It's too big That's my opinion. So truck stop at the corner of highway six and highway 60 And there's this so truck stop with shell fuel here I don't know if you can actually get fuel or not This is actually quite a big parking lot here. You'll see it on the map when you look it up Oh, there's a big pile of garbage behind there. Oh That will attract the bears Why would that be there? okay, well, I Was gonna say when you come here.  So just it's about you and if you pull in here I mean, I'm certainly nice enough store, but all of this to the left here. It didn't clear for some reason I was a little bit disappointed when I pulled in here have a nice little break, but there's Really the only way to park here is by blocking one of the pumps Or by blocking the driveway itself very strange But they do have a convenience store there and gasoline And diesel fuel just don't make plans to stay there overnight or anything. I guess Highway 16. This is a pretty remote stretch of road from here to What's the highway that we're gonna meet up with that goes up into the paw? Can't think of it off-hand right now. I know what it is. I can't think of it But this is Highway 16 is very remote. So make sure you got enough food and water warm blankets You probably won't have cell service for a long stretch down here Very icy to very Highway 10 that was the highway I was thinking of You know this guy here checking his load Go around him. We got another guy up here. It looks like he's broken down At least, you know, if you broke down it looks cell service here and you're right close to that convenience store Hugo says they come from a rough town not even on the same page They're not even in the same book. It's me something totally different. I actually can't say that Chicago's actually treated me very well It's been spent quite a few your destination is on the left Thank you. I've gathered that that's why I'm stopping with my signal on and turning too excited about this because it's very Secluded out here. It's gonna be cold and grab our fuel and we'll make our decision a little bit further Little bit further. All right. I came back into town. I just didn't feel comfortable sleeping at that petrol pass I grabbed fuel there But it's it was literally I think four miles out of town With nothing there. There's a few other trucks sitting there. That's true but if my truck stopped running for whatever reason or broke down or Whatever, I couldn't run it And my bunk heater stopped working at the same time I'd have to walk four miles and minus 30 with diesel Carrying him obviously because his paws would get cold And I don't know if I could carry him for four miles or how long would that take us I could wrap him in a blanket and I got enough clothes to stay warm. So I know we'd live But man, that would be a Wouldn't be a good experience So I came here there's a shell this guy's gonna pull in here, too Shell gas station and I went inside and asked if I could park here for the night And I'll be out of here in the morning and they said yeah, no problem. No problem Okay, thank God because I really want to stay in town here. I mean there's an inn across there I think there's a casino over there behind these trucks here. There's a 24-hour gas station So if If the worst happens, which I'm not expecting it to but just in case I Know that we'll be okay. I don't know how to walk four miles in minus 30. I mean I've learned my lesson Before it's not a good idea.  He ate his whole supper The thundershirt has been working great. I took it off for a section of time today While we were going down a pretty rough road and he did seem to get a little bit nervous He was sort of staring at me more you could tell when you know They get that bug-eyed look like something like they're just their anxiety is starting to kick in so I stopped I put his thunder shirt on and immediately just laid down and just relaxed So the first full day of using it on the road and it seems to really be helping We'll try it again tomorrow See who works again, and then we'll try it for a couple of weeks And I got my wife calling. I was just gonna call her she called me first So we're safe. We're warm and work together right weasel.  I Was rude rude work together, right Two old buddies back on the road man long on the road here, but he loves the truck Just this truck scares him a little bit. I think cuz it's a little noisier than the other one.   So thanks for watching We'll see you tomorrow right here gonna deliver in the paw first thing and we're gonna head back to Winnipeg there's a load waiting there for me. I Believe they're gonna try to get it picked up for me. It's in Winnipeg, but I don't think I'm gonna be able to get there before business closes So again, they're gonna try to get it picked up for me and bring it to our yard So I can just hook on to it there I have no idea where it's going from what I've heard with the other drivers that are doing the same stuff Sounds like it might be going to Grand Forks, North Dakota. I Don't know because we'll find out tomorrow. I don't really care where it's going They know that we have an appointment on the weekend, so I won't be going too far a lot of appointments As we're nearing the end of the third trimester, I have to balance working my tail off But also being home for the important appointment appointments I have to miss some of them and that's the hard part You sort of got to pick and choose which appointments you're gonna be at And I think this is never gonna end from now on the rest of my life Because there's always gonna be important events and dates in my kids life You know, there's gonna be the band concerts, the hockey games, the soccer games, whatever they choose to do If they're in gymnastics or have a little girl in ballet, whatever They end up doing with their lives. I don't care what they do as long as they're happy I don't want to be there And I want to experience that with them I want to be there with them, but I know as a truck driver Sometimes I'm gonna have to miss these important events and it's gonna be very hard for me, but We've got to put food on the table, but I will make one promise and One that I'll stick to is that the very important dates, the most important dates, I will be there I'll see you guys tomorrow. Don't forget to hit that like button, subscribe and hit that bell So that you get notified when my next video goes up. I'll talk to you later


Not on it. By Andrew Barton Patterson. Read for LibriVox.org by Bruce Gachuk. The new chum's polo pony was the smartest pony yet. The owner backed it for the cup, for all that he could get. The books were laying fives to one in tenors, and you bet he was on it. The bell was rung. The nags came out, their quality to try. The band played, what-ho, Rubbo, as our hero cantered by. The people in the ledger stand cried out, Hi, Mr. Hi, are you on it? They watched him as the flag went down. His fate is quickly told. The pony gave a sudden spring, and off the rider rolled. The pony finished first all right, but then our hero bold was not on it. Not on it. By Andrew Barton Patterson. Read for LibriVox.org by Caitlin Buckley. The new chum's polo pony was the smartest pony yet. The owner backed it for the cup, for all that he could get. The books were laying fives to one in tenors, and you bet he was on it. The bell was rung. The nags came out, their quality to try. The band played, what-ho, Rubbo, as our hero cantered by. The people in the ledger stand cried out, Hi, Mr. Hi, are you on it? They watched him as the flag went down. His fate is quickly told. The pony gave a sudden spring, and off the rider rolled. The pony finished first all right, but then our hero bold was not on it. End of poem. This recording is in the public domain. Not on it. By Andrew Barton Patterson. Read for LibriVox.org by Craig Franklin. The new chum's polo pony was the smartest pony yet. The owner backed it for the cup, for all that he could get. The books were laying fives to one in tenors, and you bet he was on it. The bell was rung. The nags came out, their quality to try. The band played, what-ho, Rubbo, as our hero cantered by. The people in the ledger stand cried out, Hi, Mr. Hi, are you on it? They watched him as the flag went down. His fate is quickly told. The pony gave a sudden spring, and off the rider rolled. The pony finished first all right, but then our hero bold was not on it. End of poem. This recording is in the public domain. Not on it. By Andrew Barton Patterson. Read for LibriVox.org by Chad Horner from Balli Clare in County Antrim, Northern Ireland. Set jaded in the north-east of the island of Ireland. The new chum's polo pony was the smartest pony yet. The owner backed it for the cup, for all that he could get. The books were laying fives to one in tenors, and you bet he was on it. The bell was rung, the nags came out, their quality to try. The band played, what-ho, Rubbo, as our hero cantered by. The people in the ledger stand cried out, Hi, Mr. Hi, are you on it? They watched him as the flag went down. His fate is quickly told. The pony gave a sudden spring, and off the rider rolled. The pony finished. First all right, but then our hero bold was not on it. Not on it. By Andrew Barton Patterson. Read for LibriVox.org by David Lawrence. The new chum's polo pony was the smartest pony yet. The owner backed it for the cup, for all that he could get. The books were laying fives to one in tenors, and you bet he was on it. The bell was rung, the nags came out, their quality to try. The band played, what-ho, Rubbo, as our hero cantered by. The people in the ledger stand cried out, Hi, Mr. Hi, are you on it? They watched him as the flag went down. His fate is quickly told. The pony gave a sudden spring, and off the rider rolled. The pony finished first all right, but then our hero bold was not on it. In the poem this recording is in the public domain. Not on it. By Andrew Barton Patterson. Read for LibriVox.org by Newgate Novelist. The new chum's polo pony was the smartest pony yet. The owner backed it for the cup, for all that he could get. The books were laying fives to one in tenors, and you bet he was on it. The bell was rung, the nags came out, their quality to try. The band played, what-ho, Rubbo, as our hero cantered by. The people in the ledger stand cried out, Hi, Mr. Hi, are you on it? They watched him as the flag went down. His fate is quickly told. The pony gave a sudden spring, and off the rider rolled. The pony finished first all right, but then our hero bold was not on it. End of poem. This recording is in the public domain. Not on it. By Andrew Barton Patterson. Read for LibriVox.org by Elizabeth Onder. The new chum's polo pony was the smartest pony yet. The owner backed it for the cup, for all that he could get. The books were laying fives to one in tenors, and you bet he was on it. The bell was rung, the nags came out, their quality to try. The band played, what-ho, Rubbo, as our hero cantered by. The people in the ledger stand cried out, Hi, Mr. Hi, are you on it? They watched him as the flag went down. His fate is quickly told. The pony gave a sudden spring, and off the rider rolled. The pony finished first all right, but then our hero bold was not on it. End of poem. This recording is in the public domain. Not on it. By Andrew Barton Patterson. Read for LibriVox.org by Florence Short. The new chum's polo pony was the smartest pony yet. The owner backed it for the cup, for all that he could get. The books were laying fives to one in tenors, and you bet he was on it. The bell was rung, the nags came out, their quality to try. The band played, what-ho, Rubbo, as our hero cantered by. The people in the ledger stand cried out, Hi, Mr. Hi, are you on it? They watched him as the flag went down. His fate is quickly told. The pony gave a sudden spring, and off the rider rolled. The pony finished first all right, but then our hero bold was not on it. End of poem. This recording is in the public domain. Not on it. By Andrew Barton Patterson. Read for LibriVox.org by Graham Scott Cheltenham England. GrahamScottAudio.com. The new chum's polo pony was the smartest pony yet. The owner backed it for the cup for all that he could get. The books were laying fives to one in tenors, and you bet he was on it. The bell was rung, the nags came out, their quality to try. The band played, what-ho, Rubbo, as our hero cantered by. The people in the ledger stand cried out, Hi, Mr. Hi, are you on it? They watched him as the flag went down. His fate is quickly told. The pony gave a sudden spring, and off the rider rolled. The pony finished first all right, but then our hero bold was not on it. End of poem. This recording is in the public domain. Not on it. By Andrew Barton Patterson. Read for LibriVox.org by J. D. Gibson. The new chum's polo pony was the smartest pony yet. The owner backed it up for the cup for all that he could get. The books were laying fives to one in tenors, and you bet he was on it. The bell was rung, the nags came out, their quality to try. The band played, what-ho, Rubbo, as our hero cantered by. The people in the ledger stand cried out, Hi, Mr. Hi, are you on it? They watched him as the flag went down. His fate is quickly told. The pony gave a sudden spring, and off the rider rolled. The pony finished first all right, but then our hero bold was not on it. End of poem. This recording is in the public domain. Not on it. By Andrew Barton Patterson. Read for LibriVox.org by J. D. Gibson. The new chum's polo pony was the smartest pony yet. The owner backed it for the cup for all that he could get. The books were laying fives to one in tenors, and you bet he was on it. The bell was rung, the nags came out, their quality to try. The band played, what-ho, Rubbo, as our hero cantered by. The people in the ledger stand cried out, Hi, Mr. Hi, are you on it? They watched him as the flag went down. His fate is quickly told. The pony gave a sudden spring, and off the rider rolled. The pony finished first all right, but then our hero bold was not on it. End of poem. This recording is in the public domain. Not on it. By Andrew Barton Patterson. Read for LibriVox.org by J. D. Gibson. The new chum's polo pony was the smartest pony yet. The owner backed it for the cup for all that he could get. The books were laying fives to one in tenors, and you bet he was on it. The bell was rung, the nags came out, their quality to try. The band played, what-ho, Rubbo, as our hero cantered by. The people in the ledger stand cried out, Hi, Mr. Hi, are you on it? They watched him as the flag went down. His fate is quickly told. The pony gave a sudden spring, and off the rider rolled. The pony finished first all right, but then our hero bold was not on it. End of poem. This recording is in the public domain. Not on it. By Andrew Barton Patterson. Read for LibriVox.org by M. Blankenship, a.k.a. Cat Wizard. The new chum's polo pony was the smartest pony yet. The owner backed it for the cup for all that he could get. The books were laying fives to one in tenors, and you bet he was on it. The bell was rung, the nags came out, their quality to try. The band played, what-ho, Rubbo, as our hero cantered by. The people in the ledger stand cried out, Hi, Mr. Hi, are you on it? They watched him as the flag went down. His fate is quickly told. The pony gave a sudden spring, and off the rider rolled. The pony finished first all right, but then our hero bold was not on it. End of poem. This recording is in the public domain. Not on it. By Andrew Barton Patterson. Read for LibriVox.org by Max Reichlich. The new chum's polo pony was the smartest pony yet. The owner backed it for the cup for all that he could get. The books were laying fives to one in tenors, and you bet he was on it. The bell was rung, the nags came out, their quality to try. The band played, what-ho, Rubbo, as our hero cantered by. The people in the ledger stand cried out, Hi, Mr. Hi, are you on it? They watched him as the flag went down. His fate is quickly told. The pony gave a sudden spring, and off the rider rolled. The pony finished first all right, but then our hero bold was not on it. End of poem. This recording is in the public domain. Not on it. By Andrew Barton Patterson. Read for LibriVox.org by Patrick Andrew Varnava. The new chum's polo pony was the smartest pony yet. The owner backed it for the cup for all that he could get. The books were laying fives to one in tenors, and you bet he was on it. The bell was rung, the nags came out, their quality to try. The band played, what-ho, Rubbo, as our hero cantered by. The people in the ledger stand cried out, Hi, Mr. Hi, are you on it? They watched him as the flag went down, his fate is quickly told. The pony gave a sudden spring, and off the rider rolled. The pony finished first all right, but then our hero bold was not on it. End of poem. This recording is in the public domain. Not on it. By Andrew Barton Patterson. Read for LibriVox.org by Phil McCullough. The new chum's polo pony was the smartest pony yet. The owner backed it for the cup for all that he could get. The books were laying fives to one in tenors, and you bet he was on it. The bell was rung, the nags came out, their quality to try. The band played, what-ho, Rubbo, as our hero cantered by. The people in the ledger stand cried out, Hi, Mr. Hi, are you on it? They watched him as the flag went down, his fate is quickly told. The pony gave a sudden spring, and off the rider rolled. The new chum's polo pony was the smartest pony yet. The owner backed it for the cup for all that he could get. The new chum's polo pony was the smartest pony yet. The band played, what-ho, Rubbo, as our hero cantered by. The people in the ledger stand cried out, Hi, Mr. Hi, are you on it? They watched him as the flag went down, his fate is quickly told. The pony gave a sudden spring, and off the rider rolled. The pony finished, first all right, but then our hero bold was not on it. End of poem. This recording is in the public domain.


Thank you very much for coming and again a thank you to the transnational environmental crime project for hosting me as a visiting fellow This talk I'm going to be doing today is actually a joint piece of research between myself and you might know Professor Nigel South at University of Essex and this draws on some of his research from several years ago And then research that I've conducted as well So what I'll be doing is comparing these two black markets wildlife trafficking and drug trafficking How I'll lay this out first. I'll talk about the methodology of how we actually ended up comparing the two different black markets Also, why we bothered what what is it about drugs and wildlife that can we can learn from each other? Or what does one have to speak to the other about? And I'll get into the bulk of that data so the overviews would we look at the facts around drugs and wildlife? What what do they look like? How are they similar and how are they different and then our findings of what we found from this? So the methodology as I said draws on on two pieces of research It first drew on on my case study research that I conducted in Russia far East and that was two case studies as been mentioned one of Falcons and one of Burr and How I collected the data for these case studies was was in three ways The first thing that I did is that the convention on international trade in endangered species of wild fauna and flora More commonly known as CITES has a very thorough robust online trade database So each party that becomes a member of CITES, which is about I think it's a hundred and seventy eight countries now They have to report every year about all legal trade to the secretariat that's in Geneva and part of that data set is also everything that they found to be illegal, so And this is the range of wildlife and that's what's interesting about wild life black markets and wildlife trade in particular Is how diverse it is so it's live animals and plants derivatives products and those derivatives and products take on enormous range of diversity clothes Traditional medicines decorative objects carvings bark teeth that the list goes on But all of that gets reported to the CITES secretariat and we can all access this database online for free So that was the bulk of the data and then also a content analysis of newspaper articles And this was both in English and Russian languages to see what in the Russia Faris They're actually talking about are they reporting other kinds of wildlife that's getting trafficked in different areas and then finally I've conducted 21 semi-structured interviews and this was with Russian experts so local NGOs in Moscow and Vladivostok as well as embassy officials of the US embassies CITES officials customs agents police officers basically anyone who would talk to me I was trying to to collect data about what they perceive the structure of Wildlife trafficking in Russia Far East was So based upon that data Nigel and I then compare that to his research that he endured Conducted in 1990 and you'll see that they've come up with these typologies of Traffickers and we take those drug trafficking typologies and we apply them in the context of wildlife Again, why did we do this? What's the purpose of that? Well, as you might know already There's a tremendous amount of research around drug trafficking, but possibly very little around wildlife Trafficking so we thought that we could gain further insight Into the illegal wildlife trade by comparing it to drug trafficking Which is much more thoroughly researched The intent is to contribute not only to the studies drug trafficking But it's also its relationship with other form of organized criminal enterprises So do we learn something about organized crime when we look at drug trafficking and can that teach us about organized crime in the context of wildlife? and then obviously or hopefully as many of us do with the eight the Main aim is to try to aid in prevention tactics if we can learn more about these how can we actually then Contribute to policy and contributed to prevention strategies to actually help help stop these and help law enforcement and policy makers the overviews then so to give you an idea of What's happening in regards to these two traits? So as I said extensive research around drugs I'm sure all of us know that there's a huge literature around drugs and drug trafficking There's very limited research though about wildlife though. That is changing and they're there's becoming a greater amount of literature if you look at the Estimated certainly estimated amounts of profits that the two black markets have the United Nations Office of drugs and crime Estimate that the total profits at the end of the chain for drugs to be around four billion US dollars Each year four hundred billion US dollars each year. So a tremendous the biggest black market on the in the planet It's it's incredibly profitable Wildlife is estimated to be between 10 and 20 billion US dollars annually and that number varies depending on who you ask and what's included We're to take illegal timber trafficking within this figure and also Overfishing so illegal fisheries that number would most likely be bigger, but these are estimates based on Numbers that are already probably sketchy to begin with Which I'll talk a bit about a bit more about And what's also similar between the two that's kind of interesting that we thought is that they're actually categorized in somewhat similar fashions so drugs tend to be categorized and as far as danger On some levels so category a drugs category B drugs as they use in Europe for criminalizing them and then wildlife gets broken down into categories that are somewhat similar you look at species and so we can Break them down in somewhat similar patterns in similar ways The stages also have a fair amount of similarity to them So drugs to be available to the market have to be cultivated So you have to grow the plants first and then they have to be processed actually into the drug that's going to be sold Most of wildlife is that way too that you have to actually capture kill harvest Depending on what the species is the type of wildlife that is in demand and then a fair amount of wildlife actually does get Produced or manufactured into some kind of product There is is certainly a live animal trade, but then there is also these Very big black markets of as I mentioned timber for instance has to get processed fur has to get processed Traditional medicines they all have to be actually manufactured into whatever the final product is it's going to be sold So similarities there between drugs and wildlife The way that drugs and wildlife are smuggled are actually similar as well We found the the tactics that perpetrators use The first one there on the body so drugs will be either ingested or just physically hidden under people's clothing and that happens with wildlife as well that it gets hidden under clothing and In that case, Australia has some very famous pictures that always come out in wildlife Trafficking discussions of of the men wearing vests that have these egg pouches Specifically designed where they can smuggle out eggs or have reptiles wrapped around their legs taped them So they put wildlife on their body similar to as I mentioned drugs People use their luggage. So just actually carrying it on planes or boats in their vehicles Using their vehicles to smuggle the drugs or the wildlife This can be very elaborate having hidden compartments in cars when you're crossing borders But a lot of the same tactics being used Pretty much common throughout both of them and arguably for other black markets as well Bribery is a is a integral feature to how wildlife and drugs are both smuggled and this could be Customs agents police officers border agents all of these people are are bribed to be able to smuggle these these different articles Where we start to see differences though is that drug trafficking? has a very particular Structure to it because it's never legal drugs are never legal cocaine is never allowed to be smuggled or to be transported Haraway and all of those How that difference from wildlife is that some wildlife? It's perfectly legal to be transporting it to sell it. So what happens in wildlife then is there's a lot more Laundering or forging of paperwork to actually be able to do it for instance in my work around Falcons It is perfectly legal to farm falcons so you can raise them in captive-bred facilities That's perfectly legal in your cities It isn't legal though to take a bird from the wild But what will happen is paperwork about this bird being captive can just be given to a wild species It's actually been taken illegally. So this forging paperwork actually allows wildlife to be laundered So where it is actually illegal it looks like it's legal because the way the way the paperwork works This isn't going to happen in drugs.  Well, they're happy massive packages of drugs where they'll Have a helicopter or a small plane Fly to where they want it to know just drop it out into the middle of the Arizona desert for instance in the US So you have these very elaborate kind of smuggling things that happen with drugs There is certainly some elaborate smuggling of live animals But but we argue that there's there's some difference here in perpetration because of the pure illegality of the drug trade a bit more about the figures that I presented earlier, so The estimates of this 400 billion dollars a year or how much drug The the actual quantities of drug that actually get Smuggled these are estimates just based upon assumptions where they Get these assumptions from where the estimates are based on is the turnover of drugs the amount of trade and the profits and What the different figures of the UNODC comes up with then is that at that first stage At production when it first gets sold drugs are worth around 13 billion Dollars a year, but at the next stage at the wholesale when it's getting ready to go to the final buyers It goes up to 94 billion and then you have another increase up to around 334 billion at the final stage and these are all based on Retail values that we would that experts have gone in and estimated how much is act or how much it's worth at those different stages Wildlife has similar stages the trouble is then though What gets reported about wildlife is usually What the perpetrator is saying that the value of the wildlife is worth and they don't have an interest in actually reporting Actually, how valuable it is so it's based our estimates are based upon these customs figures Rather than a market value of actually how much profit they would be receiving from selling the wildlife Other problems with the sort of data that you get around Prophets and how much it's worth estimating how large it is is that the incredibly hidden nature of it and this idea of Who are the victims in wildlife trafficking obviously wildlife isn't reported that they've been trafficked So you're reliant upon people coming forward or being uncovered by customs and border agents There's also no set standard value to wildlife what one person or one country would regard for their timber say Is it necessarily going to be what it's worth in another country or to other people so Without a standardization of value of wildlife this 10 to 20 billion a year. It is it's really really just an estimate and Then I argue another work as well that this doesn't also cover Intrinsic values that we can talk about wildlife that this is purely a market kind of consumer Focus or conception of what wildlife is worth. What about other? Value that wildlife has to us as people aesthetic values Ecosystem services. What about the destruction to environmental places and and that's not captured in this kind of Just purely for profit kind of value that we're assigned so just the two examples that Generated from my case studies the fur Trade is organized in the following way at least in the Russian part race where I collected my data You have hoaching with what's called indiscriminate traps and indiscriminate traps just refers to the fact that this Connambare steel trap That just lays open can catch any sort of animal. That's why it's indiscriminate. It isn't Species specific you can catch any sort of thing that steps into it What happens then is the trapper will take that pelt of that animal and sell it to a middle man What might happen though is that because this trap is indiscriminate maybe he caught something that he wasn't supposed to I say he because I don't think there's many women trappers as far as I can tell but they then What if they've caught a tiger which is certainly possible in Russia or a leopard something? That's totally illegal that they're not supposed to have in those rare instances The trapper can't pass it on to a middleman because there is an illegal market for a tiger skin What happens in the Russian Far East is if you do actually catch something that's endangered You're very close to the Chinese border and you you cross over into China and sell it there If it is something legal though, it's a fox. It's a sable You sell it to the middleman and the middleman carries on selling it down the chain So there's a huge market for fur in Russia. So all of these pelts get collected for the annual auction every year what's found though is that There's quotas set for sable, which is the most popular fur in Russia and every year All of us could go and look right now at the st. Petersburg fur auction website The amount of fur that is sold exceeds the set quotas So there's a legal tracking going on there as well Even though it's legal to take sable more sable than is supposed to be taken is taken every single year So there's illegal blended in with the legal as well even in the week in these Very prestigious fur auctions And then as I mentioned in the third point the selling on the black market if you catch an endangered species It's got to go to the black market. You can't go into the into the legitimate fur industry Structure very differently than though if you look at Falcon trade, which is a live trade People in the Middle East and others argue also in the Far East in Japan where Falcon re still practice So using Falcons to hunt It's a very popular sport still in those countries in those areas So there is this demand for live birds to participate in this activity How that happens then though is you need a very specialized person an ornithologist or someone with a Intricate knowledge of birds to actually go and collect eggs for kept your young birds out in the wild so it becomes a very specialized kind of task and particularly it isn't just someone will go out and collect eggs it usually Looks like it's filling a specific demand So someone wants a bird they've asked through their network and it gets funneled to this ornithologist Who then goes out and actually takes takes the egg or takes the unbird? Healer then picks it up and transports it these really long distances. So traditional Falcon Falcon range used to be in Central Asia, but most of the populations there have been completely decimated So the last sort of remaining populations are in Russia and very remote regions in the Far East and so you have this Epic kind of transport going on thousands and thousands of miles of the bird getting from Far East Russia into the Middle East This is usually happening by trains boats or planes But key to that as I mentioned before is bribery you need to be bribing customs officials along that route or you need to be bribing transportation people along that route so that the train employee or the Airline employee will let you take something on that's illegal So how they differ then that the fur can be very individual. It's opportunistic You might take more than you're allowed to while you're out there or accidentally Kill something that's endangered It goes through the middleman into the industry so it gets laundered into a legal Business most of the time though. There is that black market that I mentioned of endangered skins Falcons and it takes on a different structure. It's a very specialist. It's very planned Dealers smuggling along very specific routes of one location to the destination location And the experts that I talked to argue that organized crime was involved in this and They didn't think that it was the actual the Russian mafia so to speak But they argued that it was more organized crime coming from the Middle East that would organize the the actual Transportation route in the smuggling route Again, that's all very anecdotal There isn't any really hard evidence around there, but then they do think that organized crime Facilitated this simple chain network of smuggling How then? Does that compare to what previous research around drug trafficking actually showed? so Nigel and his colleague Dorn argued back in 1990 that there's two sets of Category where you could break down the drug trafficking and that was multi commodity Actors that are involved and then specialist actors that are involved And part of the multi commodity they argue are these business side liners as they called them so You're involved in dealing drugs, but it's not your main business You do it as part of other illegal activities And we found that that fit very well with the fir tree that I just described then It isn't your main intention or your your purpose to be engaged in illegal black market behavior But you might do it as a sideline to other legal activity that you're actually involved in So business side liners is one that we felt you could see Correlation to between drug trafficking and wildlife trafficking also in the multi commodity Actors you have the criminal diversifier. So someone who is involved in drug trafficking It's argued and you see a lot of literature is a very rational choice You make a lot of money from selling drugs So it's an economic choice that you engage in Particularly for the profits Where we saw correlations there is you do see Drug traffickers that are actually moving into wildlife and arguably for the same kind of rational economic reasons And we see this mostly Coming out of other literature not from the research that I did but there's quite a few reports coming out of Brazil in particular where Customs agents estimate there that around 40% of all Drug seizures actually have some amount of wildlife in them. So you have What they're arguing is clear evidence there that the drug traders are making this very rational economic choice to Diversify and get involved in other kinds of black market profits You also have the in regulars in the multi commodity actors and these are what I referred to For wildlife is the indiscriminate trafficking. So it's an opportunistic kind of thing rather than an Intent or a specific purpose You also see that in drug trafficking at the street level where there's market opportunities You aren't necessarily intending to be a street corner drug seller, but the opportunity arises It's profitable. So you might take advantage of it. So again Opportunistic irregular is where where you might have the opportunity to make money in a legal fashion as I mentioned then that South and dorm breakdown the actors from the multi commodities that I mentioned and that there's also the specialists people that are involved in drug Trafficking or as we argue wildlife trafficking for very specific reasons and in this case you could see that for the smuggling to order for the wildlife that a Person in the Middle East has a desire to have a very particular kind of Falcon and they Use their network to have that order placed in the drugs you see that it in friendship networks that often Help to perpetrate drug trafficking that it's the people that you know that you facilitate that through it isn't necessarily a You're not dealing with strangers ever you're dealing with people that you know So it's a very specialist kind of thing You know where to get the particular drug you're going or that you'd like So you use your friendship networks to actually obtain it Trading charities is another of the categories that South and Doron present and it's this quasi Ideological commitment. So there are people that are using drugs because They believe everyone should be allowed to use drugs and that they will use marijuana for instance regardless of its illegality and they're not doing it as some Resistance only doing it because it's illegal, but they're doing it because they think it should be legal and that they're going to continue using it Anyway, we argue that you can see the same in wildlife in the fact around traditional medicines that people particularly eastern cultures that believe that traditional medicines are Valuable in terms of health and in curing certain illnesses. They do so out of an ideological commitment It's a cultural choice to actually engage in this kind of behavior So they're not doing it again because it's illegal They're doing it because it's something that we actually believe in so that there's there's a different motivation behind it there The the illegality is is a bit of just a Side effect rather than the purpose South and Doron also had two other categories that we didn't necessarily find Correlations to wildlife they have retail specialists. So shops that are particularly for that For selling drugs that you go there because you know that that's where you're going to get it We might be able to sort of see this in traditional medicines But even traditional medicine stores that exist outside of the east those are fairly well regulated And it's difficult to know if there are black market products in those kinds of stores And the second one, we're not sure if there is a correlation is state sponsored traders So you do have some governments in narcotics and drugs in particular that are actually facilitating drug trade on some level providing the infrastructure or at least not cracking down on it and I have a question mark there around Cambodia need the evil logging There's been reports by the NGO global witness several years ago that it's actually the Cambodian government that gives the logging licenses to Particularly friends of the family or different organized crime groups, but these logging permits are in areas that are actually protected forests So it's argued that the Cambodian government is the one that's facilitating the illegal logging So that could be state sponsored again, that's Not so much anecdotal. There's fairly good evidence for it, but Further research has to be done to really to cement those kinds of ties that they're they're facilitating it so other findings that we developed around the two black markets is that the possible increasing connections between the two and Lorraine Elliott here that's running the tech project has talked about parallel trafficking of Not only drugs and wildlife, but other black markets that you see using the same smuggling routes or using experts that know The different networks using them to traffic different kinds of products There's also evidence coming out of Chatham House of the Royal Institute of Affairs in London so came in brak study that drug trafficking is actually funded by illegal logging So the connection there being the profits that you're making off of illegal wildlife Can be actually funneled into drug trafficking and drug trade And then also another study there by putting Nelson's showing that seasonal drugs and wildlife so When you're animal that you're poaching isn't in season you'll turn to drugs and vice versa So it's actually just a very physical kind of cyclical thing that you'll be doing drugs drugs or wildfire We talked about expanding this so what's known about wildlife has been so limited But has increased so I think we could revisit this and look at other kinds of wildlife Structure and we talk about looking at other frameworks. What about illegal timber or fishing in comparison to Drug trafficking and what about illegal wildlife trade in urban areas that might be a better comparison to drug trafficking since Drug trade actually occurs in urban areas Other comparisons and ties we might look at human trafficking. Obviously, there's Again anecdotal evidence that though that is blended in as well that as humans are being trafficked wild life Or drugs can be trafficked with them and then a fairly Well the funding of terrorism and militias and this is kind of gained a bit of prominence in the last I would say two years studies coming out that they do think that wildlife in particular ivory Some illegal logging might actually be used by terrorists As a way of gaining funding so that they can buy arms or fund their militias So some ties there for the research that might be might be interesting What we really would speculate at the end though is is what can we learn from the fight against the drug trade? right the war on drugs and very recent reports have talked about that Maybe most of the world would agree. It's probably a failure. So what can we learn on how we've? Attacked this drug problem. What can we learn in trying to deal with wildlife? so the move in in the war on drugs a discussion sometimes revolves to should we decriminalize it and Then the discussion turns on wildlife should we also decriminalize the trade in some wildlife and that's an ongoing debate and just within Just last month in March the Sides has its Convention that happens every few years and the discussion always turns to should we allow trade in ivory rhinoceros horn will that actually Decrease the amount of crime that's associated with it. So there's this ongoing debate of what can we learn around decriminalizing different trades What we have seen it's clear coming out of a study in 2009 is that For instance if we turn to the US if you look at the amount of resources dedicated to fighting both of these The drug enforcement agency has 5,000 agents in the US That's how much effort they have put into the war on drugs The US Fish and Wildlife Service though, which is the only agency that really deals with wildlife trafficking Excuse me only has 200 agents. So there's an incredible amount of resource differential there Which says something about priority that we're very concerned about drugs not necessarily so concerned about the illegal wildlife trade and and Also what we can look at in terms of of drugs versus wildlife is is the punitive nature of how we've gone with drugs giving huge jail sentences and Fines is that the way to go? Is that how we should be approaching wildlife? Which currently has very little punishment small fines that are argued don't deter people from actually trading it So I would argue no, we shouldn't go the way that we've gotten with drugs, which is incredibly punitive and has created this huge jail culture But maybe it's not where we're at it wild either where we have very limited penalties and and these are Some of those final thoughts from from the research that we did Thank you


Hey guys, we are back with another art room organization video. I know For this one, we're gonna have to get on the floor, but to be honest with you I'm too old to get on the floor if I get down there. I can't get back up again So I'm gonna grab the stool and I'm gonna sit down. All right, I'll be back So I'm sitting on a stool on the floor near that Why? Because we're gonna talk about paint and by association coloring supplies, I suppose So I have a specific way I Store my paints and coloring type supplies that really works for me. It's a new thing to the new space It's not the way I did it in the old space, but it definitely works here So I do have two Ikea carts and then the old space used to store a lot of my coloring supplies there It didn't really work for me, especially in the new space. It didn't but I have this wall back here Which I've put this some more of this white wire shelving on which I love which is from Target And then I've been Or boxed supplies by category. So let's review that shall we let me turn the camera. Okay lighting might be a little funny I do live in the Portland, Oregon area and It's overcast again outside Anyway, as you can see by these first few bins, I do have things Marked by category like these are just plain acrylic paints these as these are They're both acrylic paint and they're both marked as such But the clear bin is more artist acrylic paint things that you would use if you were painting a canvas These are more craft acrylic paints things you would use if you were just working in a journal or doing a craft project The this little bin in between is just because I had nowhere else to put that and I've got my museum wax Anybody who's lived in earthquake country, you know what that is. It's just stick your breakables down to your shelf So when you have an earthquake things don't readily fall over and break and then I have coarse salt ice cream salt in the back that I use for doing textures and some Painting things like with watercolor. This is another bin of acrylic paint, but this is mainly Dean awakening paint This here is specialty paints like fabric paint and actually I have nail polish in there Overflow of the art paints and actually white flow acrylic in the back I use the spaces between the bins to store sort of overflow until I use them up This down here is artist paints. These are made well except that decor Decorate bottle in the front. These are mainly golden paints golden fluid acrylic paints and golden fluorescent paints metallic paint ink We do have mediums over here. So we have glues finishes whoops where we have thin glues finishes This is again is overflow, but it's mostly pouring mediums Then we have just a general mediums tub Ink pads and yesterday. I was just fiddling around some stuff. So I've got this little tray stuffed in here of foils and foil glue which actually doesn't belong there So ink pads. I have these bins. These are specialty paints like acrylic wash My tubes of watercolor and that's that Daniel Smith tube bin Oil pastels whoops, wait a minute oil pastels soft pastels neo color twos and sharpies alcohol inks and reinkers Sprays don't have too many. I mean I still have to more than I need but there's not a ton. I don't think Some glitter glues and clear gesso again overflow Glitter and texture All your glitter textures foils and that's actually where that other bin goes Gesso and grounds and Then embossing stuff powders glue beeswax All that stuff All right up here We have a little another five drawer set of drawers and I've got powdered pigments I have a some acrylic paints. They're all just shades of gray some crayons and some liquid pencil again Which are shades of gray paints these are my Pen and pencil storage by color, which I love and was from a YouTube video That I saw here a While back and I love it so anything that I have pen or pencil wise that's red With a few exceptions, which I'll show you in a minute is in that red and pink bin So when I'm working on a journal page, I'm like, you know, I need to add some purple pencil I just go grab that purple bin and bring it to the table. It works great here. I have Extras of some of my favorite pens these are just overstock So when I run out of say a big pen at the table I run out of ink I just come over here and grab another one. So these first few cubby holes. That's what those are then as we including paint pens Because I have like one of each at the table But when I run out of paint in the pen, it dries up I come over here and grab a new one then as we go along we have the bigger fatter pens Which don't fit in the little cubby drawers mainly your big brush Paper is a favorite custom the pit pit pens favorite castell pit pens and also permapake Pens your chunky colored markers are over here. The little drawers underneath are just random things like paper bead supplies and leads and grinders and Empty watercolor paint pans and stuff like that up here By the way, here's the clock and it's going to be for sale. Somebody's interested message me I did that a while back Anyway, these are all crayons like water soluble crayons. So you've got your distressed crayons You've got your rose art crayons and highlighter crayons, which are water soluble and then you've got a bunch of these kind of sparkle crayons and I think there's some artists loft Like the watercolor crayons in here This is just something that just came in I need to put it away Another powder pigment which doesn't add some other ones that don't fit in that drawer over there Colored pencils and chunky paint pens, which I'm trying to use up Short colored pencils In colors and graphite, which I love to use and I usually use together So these are in this bin and again these come out and I can just bring this whole bin over to the table these are sort of spare multi pens and These sort of these are by Koi. There's the trying triangular multicolor color pencils and Pencil sharpener a random wooden stamp and some more powder pigments And down here in case you missed it. This is where I store all my watercolor paints I've labeled them for the most part on the side so I know which set is which because I do have quite a few of them in my collection and That's that this storage system works. I'm not showing you the details of the paint storage so I can brag about how much I have That's not the point and I built up my collection over years literally decades The Daniel Smith paint collection alone I would go into the art supply with 20 bucks and buy sometimes one tube of paint and it took me a long time to build up my collection I Also made a few mistakes like in the beginning I would take a class and they would say you need neo color twos and I thought you needed them all So I would buy just a set that had all of a minute You don't need to do that go out and find your local art supply store Even if it's a little bit farther away where they have open stock go get just a few in your favorite colors and go from there There's a tip. So anyway, this is how I store my paint coloring supplies. It works for me It works in my space. What do you do and what works for you? Let's share not only here in the comments below but over in the Facebook art groups Let's see what happens and I would love to see what you do. I'm going to link other videos of some of my friends and Fellow artists here on YouTube have done similar videos to see what they've done and I'm going to link them in the description below So show everybody some love like share and all of that stuff Hit the little bell icon if you want notifications and new videos because if you don't do that You won't get notified If you want to follow me on social media see what my daily art practice is go join one of those said art Facebook groups Click on the link tree listed links in the description below and you'll find all the places You can find me and support my free content here on YouTube and over in Facebook. There's a bunch of different ways There's also my happy mail address and a bunch of other stuff So check out that description the most important thing of course is to go out and have a great day Do something nice for yourself because you deserve it. I'll see you later. Bye guys