Hello everyone Today I will talk to you. Yeah, exactly about how to what we do. I trust you to make it possible for travelers all over the world to Find their perfect hotel So just something about myself I'm from Italy from more than I Italy. I have a background in both computer science and mathematics, so I am both let's say a computer science is a mathematician or maybe none of them I I Then did my PhD in cars through a Germany the topic of my thesis was Basically development algorithms for network analysis and I defended my thesis last year in December and since February I'm now working as a data scientist that trusts you with a focus on natural language processing and algorithms and machine learning So What is it the question that the major review team at trust you so the team I'm part of Is working on so what is the problem? We're we're working on so what we want to do is for every hotel in the world We want to provide a summary of Traveler reviews and why would we want to do this? Why won't want to provide a summary? Well It if I guess most of you whenever you book a hotel you first look at Reviews from other travelers and if you think about it This is quite amazing that we can do this because maybe 20 years ago or 30 years ago There was no way of doing this so whenever booking hotel what people could do was basically only pick some hotel based on Yeah, basically how it looks from the outside or just listening from some some Recommendation from friends who had already been to the city maybe And what we have now is we basically it's like having maybe for a city We want to go to millions of friends who can tell you their experience about the hotels in that city and Of course, we cannot on the other hand the problem is that we cannot really read millions of reviews whenever I wanted to whenever we want to make a decision about in which hotel to go to and What most people do is they just read a few reviews go through a few reviews and then make their decision based on that but of course different travelers also have different expectations and They look for different things in hotels So it might be that the experience of the few reviews we read from other travelers does not really reflect our expectations in a hotel So what we want to do is basically provide a summary containing all the relevant information from all the reviews we can find for for each hotel all the reviews you can find online and Make it like a short summary so that people can can read it and Immediately get all the important information about the hotel So these are these are a few examples of information we can show about hotel We can show we can be interested for example in how the building of the hotel is If it's modern clean about the view Then the hotel has if it's good for parting for example And for example another like we can also show information such as solo travelers complain about TV. So this is the also kind of detail we can provide and Oops, I wanted to so yeah, I just wanted to show how it looks from the From our website. So this is basically Can you see it? No, you can't Okay, yeah, all right, maybe I'll show it later, but let's go on with this so The disinformation is not only provided on our website. We also Provide disinformation to other websites. You probably know kayak This and you might have seen this on kayak already So kayak can can show you information about hotels for example the amenities the vibe location so this information is all data we provide to to kayak and And not only kayak but also Google so whenever you Google for hotel You see Basically, you might have seen this already on Google. So this is also data. We provide for example here we show the score given by different types of travelers and and also This is shown by Google is also data we provide and it's kind of a shorter version of our meta review so basically what we show here is Yeah, like a summary for example of the rooms guests like the the rooms and for example, they notice that Maintenance could be improved. These are examples or information about the location service and and This are a few examples basically so how do we actually get to the to the meta review? How do we Build our meta review Every week we crawl reviews from many different sources on the internet We have in our database about 620,000 hotels and we crawl quite a lot of reviews about Whenever three millions new reviews per week, that's quite a lot of data We have and we store this data in an Hadoop cluster and the first step that we Do basically on analyzing this data is the semantic analysis of it. So Basically for each review We we for for each sentence in each review we extract We map each part of the sentence to categories for example This part of the sentence refers to the bathroom or this other part of the sentence refers to the breakfast of the hotel in addition to categorizing these parts of the sentences we also Compute the sentence we compute a sentiment for them. So was the sentence positive? Was it negative and? after this part we basically Aggregate this data and we apply machine learning algorithms to to them and In the end we we generate the text that you saw before and we provide this text We showed our this text on our website, but we also provided to Google kayak and they're actually more hotels.com holiday check to several other websites So this is just to give you an overview of the technologies we use to build a meta review Of course Python so almost all of the code we write is written in Python. That's why I'm presenting this here and We use Luigi for our pipelines. We use a doop and spark for processing the large amount of data we have and We use yeah Postgres MongoDB as databases and then we what you see on the right basically are all the some of the libraries We use for machine learning just to give you an overview Okay Now that I gave you basically an overview of what we do a trust you and the meta review team does I would like to talk to you about a specific problem that my team has been working on in the in the last months and Specifically it's hotel classification So the question the questions we are trying to answer here are for example What are the most romantic hotels in town say you might want to? go to a very romantic hotel to a merry romantic weekend and you're looking for the best hotel for this or You are looking for a hotel. There is appropriate for a family holiday or here There are some more examples for example, which hotels have the best casinos the best Lakeview are the best ones if you want to go to a golf course for example or Yeah, or the best seaview. I mean there are many many questions we we can consider and Our solution of to this problem is basically Composed of these parts that I'm going to describe. So first of all we Basically represent the reviews as vectors That's the first part we need to do in order to be then able to apply machine learning algorithms to them for classification and in particular we consider two ways of Representing texts representing reviews as vectors One is TF IDF, which I'm going to present soon and the other one is Doctor vacuum beddings. I'm also going to tell you a little bit about them and After this basically as I said we apply machine learning algorithms and another thing we do is we also combine Basically the review content with geographical data whenever it applies whenever it makes sense to improve our classifications So now I'm going to talk to you about TF IDF. This is the only slide with formulas Don't be scared. I'm going to then make an example to give you an example. It's actually quite a simple method The idea behind TF IDF which stands for term frequency inverse document frequency is to Reflect reflect the importance of a term which we call T for a document D in a corpus Okay, a collection of other documents So we can introduce the term frequency, which is basically simply for term T in document D the number of occurrences of T in D divided by the total number of words in D and We might then assume that A term is important for for the document if its frequency is high Right, we could say okay if a term appears very often in document then this should be quite important, but This is not always the case because for example if we consider hotel reviews when we might have that words such as hotel They're going to appear very often in reviews. So it might not be so relevant for the specific review we are considering and So basically There's another term called inverse document frequency, which gives a higher score to words that to terms that Do not appear very often in the other documents of the corpus. So are more specific for the document. We are considering and This is simply defined as the logarithm of n which is the number of documents in our corpus divided by The number of documents in which the term T appears So yeah, this will be high if the term doesn't appear in appears only in a few documents And in the end the TF IDF score is simply computed as the product of the term frequency and the inverse document frequency So to give you an example, let's say we have a document and this document is I hope this talk is not too boring and The corpus we have is composed of I hope this talk is not too boring This talk is just as boring as filing tax returns and tax returns are not that boring so Let's say we want to compute the TF IDF score of boring and Okay, the term frequency is simply one divided by eight right because there are eight words in our document and boring appears only once so That's why we had one divided by eight But the inverse document frequency in this case is the logarithm of three because we have three Documents divided by three because boring appears in all of them. So the logarithm one is zero So what we have in the end is that the TF IDF score of boring is also zero Okay, and this makes sense if you think about it because boring is not specific for for document D, right? It appears everywhere And if we can see their hope inside we have that the term frequency is just the same as for document D as for sorry for boring it also appears one once and But in this case the inverse document frequency is higher because hope appears only in this specific document, right? So in this case, we have the logarithm of three divided by one So in the end we also have that this the TF IDF score is higher Okay, now you might be asking yourself Why do we want to compute the the? TF IDF score of terms in a document the idea is that if we compute the TF IDF score for each term we have in the corpus then We can represent Each document as a vector of the TF IDF scores of the terms there are present in the corpus So to give you an exact to consider the same example as before For document D. I hope this talk is not too boring. We have that we can't represent it as this vector that you see here so You see that a lot of in this case a lot of Terms have zero score for boring We saw it before already and for the other terms on on the right It's basically because they do not appear right they are in the other documents of the corpus, but not in D So their term frequency is zero Okay, and the idea behind this is That the TF IDF vectors of similar documents Will also be similar to each other. Okay, so if you consider for example say You have a document you have a corpus composed of recipes of different foods and you consider the TF IDF scores of two chocolate cake recipes, for example, you'll have that Their TF IDF scores the TF IDF factors will be close to each other because worse such as chocolate butter or whatever other ingredients are in a chocolate cake They will have a very high TF IDF scores for both recipes and Yeah, in our case, of course, we're talking about reviews So each document is a this the set of all reviews for a certain hotel basically and the idea is that if we have a training set so a set of Hotels for which we know that they are of a certain category for example family hotels Then we can use machine learning algorithms to classify all solar hotels and being able to say whether they are also family hotels or not Yeah, so creating training sets is quite important So we actually spent quite some time on this because if you don't really have reliable training sets then Your you can be pretty shorter.  Well, so We build them based on review content We also consider the amenities that these hotels had and when it made sense We also use geographical information for this we use open-street map, which is actually quite quite an amazing project It contains geographical information for for many many categories. So for us It was actually very very helpful examples of information. It contains our coordinates of coastlines highways We ask he lives all kinds of tourist attractions. We're also golf courses casinos really a lot of information and I will also now tell you something about Word2Vac, which is also another Yeah, we and let's say the baseline for another technique we use and the idea here is quite different from TF idea so the idea here is we Say that words are similar when they appear in similar contexts So we also take the context of words into account and not just a frequency and what is the context of a word? for example, if you consider the sentence here the context of Fox is The set of words are preceding then and succeeding Fox. So for example in this case, we have a window of two and therefore quick brown and jumps over at the context of Fox and The basic idea here is that synonyms like Intelligent and smart for example, they will appear in in similar context, right and yeah, so the basic idea to to Create a word2Vac model is to train a neural network with a with a word we're considering and then The hidden layer of this neural network will be used to to represent then the word as a vector because that's in the end what we want to do right representing words as vectors and Yeah, so we'll have that words with similar context in the end will result in similar vectors. So this is a Quite famous plot you might have seen it already actually on the website where I found it It mentioned that it's illegal to talk about word2Vac without showing it. So here's the plot and So what you can see here is quite nice is that basically word2Vac encapsulates Relations between words and in particular we have that for example The distance between between similar words is also pairs of similar words also the same so The distance between king and queen for example is the same as the distance between man and woman and we can write this this nice equation that basically King minus oh, yeah, it's king minus actually wanted to have it here king minus man plus woman is equal to Queen which actually actually makes sense Yeah, and in the end we don't want to just represent words as vectors we want to represent reviews right so documents and What we use basically doc2Vac which combines Let's say vectors of all words There are in a document into one final vector. Let's see Okay, so to give you an idea then of our how our classification pipeline works The first thing as I said we want to transform right reviews into vectors so we do this with either TFIDF or doc2Vac embeddings and we since we have reviews in many different languages we do this separately for each language and Then again separately for each language we use a classifier called gradient boosting. I'm not going to talk about it now because we don't really have Enough time for that But it's basically an ensemble of decision trees. You can take a look at the Wikipedia article or you there are many resources online and Basically in the end what we do is we combine the predictions of the classifiers for each language into one final classifier Giving weights based on the number of reviews we have into different languages So basically for a hotel we have 90% of reviews written in German and only a few Reviews in Italian then we'll give a higher weight to to the German review to the German classifier Right sounds like everything should work fine right this makes sense Yeah, it did work. It does work quite well in most cases But now just to I would just like to tell you a few examples where we a few problematic cases we have They're also kind of funny. So One problem we had is that when we were classifying golf hotels We basically Realized that some hotels That were actually not close to any golf course They they got positively classified and actually what was actually quite weird was that only the German classifier So the classifier for the German language was really confident about these hotels being Golf hotels, but for the other languages, this was not the case so we were really confused what was happening and why only Germans were talking about a golf course and Know what what then we realized is that the English word Gulf is actually golf in German. So Basically the reviews were were referring to the Gulf of Naples So they were not really viewing to a golf course, but still they the there were a lot of mentions of the word golf, of course and And another problem we had is that Basically in a small town outside Atlantic City, there were a lot of mentions of casino related terms actually a lot of positive mentions and Although there was no casino close to this to this hotel. So we were a bit confused and Basically turned out that in fact in these towns there are a bit far from from Atlantic City People were really happy about being far away from casinos like to be in a quiet place And that's why they had all these positive mentions of casino Yeah, so Basically the solutions we had these are of course quite complicated problems so But with the solutions we found so there's still room for improvement, of course But the solutions we found so far is basically to also use geographic data These also have quite a lot for example with problems such as yeah casinos Golf courses so we could also use this information to make sure that we don't classify hotels there are not close to the amenity we're considering and In addition to this we we also perform quite an extensive cross validation in order to be sure that we pick Parameters that can guarantee us a high precision and then ideas for future work we could consider actually disambiguation techniques and So this is actually an idea that a colleague gave me quite recently. So this is something we might look into and Also, we could think of combining tfIDF with word embeddings because what we are doing right now is for different categories we are just picking the one that works best between tfIDF and and doctor vacuum beddings, but Basically, there are also ways of combining both. So this might lead to even better results So this is basically what I wanted to present to you If you have any questions feel free to ask or you can also contact me This is my email address and you can also feel free to take a look at our website. So Basically there you can find a little bit about what The engineering department I trust you is doing also something about job openings if you're interested and Yeah, that's basically what I wanted to tell you. Thank you Hi, thank you for the nice talk. Do you have any intuition on when tfIDF works best and when dr. Beck works best? Well, what do we? Yeah, okay. So my my intuition and something that has been shown in Practices that dr. Beck doesn't work very well when your document is short So it's in those cases. It might be the case that tfIDF is working better Yeah, this is basically if we this is something we still have to Investigate a bit more though. So basically what we did is was mostly Comparing the two methods and seeing which one was performing best, but yeah, this is also Maybe something we can investigate a bit more like why exactly this is happening But this is quite this is certainly one of the reasons so the length of the document is Influences this quite a bit and yeah Hi, first of all, thank you. I think it was a great talk. I was wondering where you Considered that maybe many of those reviews might be fake and whether that's a problem You need to tackle Yeah, this is actually a very good question. So one thing is in general a trustee We're only considering reviews from verified sources. So for example only reviews from booking.com Where you can know for sure that one can post a review only if it this person has already as really say to the hotel or Google we also use reviews from Google. So we don't really use Google from sources from which There's no way of really identifying the person But yeah apart from this Whether a person Whether didn't use his fake or not whether sorry the reviews is fake or not This is not something we are Checking yet, but this is also something we talked about. So there's It might be that in the future. We certainly plan to consider this as well Thanks for your talk. It was really good. Do you have any recommendations for tools for labeling data? I struggle For label it for labeling data. Do you have any recommendations for tools or applications that help with that? Okay, what do you mean exactly like labeling? So say yeah, so say you've got webpages and you want to label The data you find on the HTML. Do you have any tools that help for help with that? I Not sure Yeah, yeah, so for building up for building a training data set you've got some Okay, I'm gonna start labeling that and you've got lots of them to do right. So yeah, sure. Is there any tools? We didn't really use any tool in our case. So we mostly yeah, so we considered mostly To review content information the frequency of terms and we you we looked at amenities as and things There are basically specific to our problem like specific to for example Lake hotels so the vicinity to lakes for example. So we didn't use any specific tool. So Keep looking Thank you for the great talk You mentioned that example with German where golf means two things Do you know how word to work behaves when when you have synonyms like that that fall on the same word? Like golf with different meanings. Yeah, like what what does the German golf vector look like? Yeah, that's a good question. I Mean No, I'm not sure exactly. Yeah This is yeah A difficult question. Yeah Google translator like if we can use that or what is the question? No, no, we don't so actually we really like the classifiers we consider them really separately So we don't translate any text. We have separate classifiers for German separate classifiers for English We don't use Google translate Yeah Hello, thank you for the talk. I wonder about the data Pre-processing so there is some data cleansing that you had so major one that you do so kind of clean up the data set and perhaps identify a Name of places so things like that Yeah, so I can't give you too much details about this because actually this is done by a different team This is not exactly so there's another team that really works on their performance as semantic analysis And also takes care of Yeah, cleaning the data for example removing stop words Yeah, tokenization limitization all this kind of thing. So a team there's a team that is taking care of that And So sorry your question. You asked me What we do exactly or yeah, I was wondering the Sun general ideas if you could talk a little about that So I can give you just these general ideas because I'm this is something that a different team does. I don't know if Maybe Stefan you want to add something on this or It's okay Well, maybe we can take this then offline to other also people here from my company also can give you maybe more details Okay, thank you Okay folks, we are out of time. So let's thank our speaker again


In this module, you will know that why it is insisted by gender ideology that oppositions, differences and dichotomies should be established, should be maintained. They provide some rationale behind that. And that rationale is functional. Let's see what this functionalism means. How? Because this was the question before us when we talk about sex oppositions. So first question is, how are these oppositions linked with each other? How we establish, for example, relationship between emotions and reason. And reason is assigned to men and emotions are assigned to women. How we bring these two attributes, these opposite attributes together in one link? What is the thing that connects them with each other? It is gender ideology. The answer is that it is gender ideology that relates them in popular mind. It is done, why it is done? It is done to maintain difference. And this is evident in expressions like opposite sex. Instead of saying, and other sex, we usually say opposite sex. This expression itself tells that something is natural, something is effect. And to think something, effect, whereas it is not effect, this is called ideology. This is gender ideology. And that's why it is maintained through such expressions which are present in language. And this is the way how language constructs gender. That is our course all about. This is evident in these expressions like opposite sex, like battle of sexes. It shows there is some conflict and gender gap again the same meaning. Ideological opposites are reinforced, they are supported, they are perpetuated through embedding. Embedding means when a thing lies inside and other thing, this is called embedding in general sense. To reinforce, to establish the oppositions between sexes in gender ideology, another thing is done. Not only men and female are kept apart from each other, kept on poles apart, rather within the category of men, men are compared with other men and women are compared with other women. Again, on certain oppositions. Let's see for example, small men are compared with taller men and it is usually thought that small men are less masculine. And on the other hand, large women, they are taken as less feminine among other women. But in many life affairs, men and women are complementary. Now again, we were talking about opposition and now opposition is being supplemented with complementarity. We maintain dichotomies and we establish them through language. Why? Because we need complementary roles. These complementary roles are given to opposite sexes. This is called complementarity. For example, we use this very common that this is my life partner, my better half. Here we don't see that oppositeness which we have been talking about short while ago. Here we see one completes the other. Then heterosexual attraction also creates complementarity. People of opposite sexes, they are attracted to each other. Psychology supports this point of view that opposites attract to each other because people of different sexes, they don't know. They are unfamiliar about the experiences of the opposite sex. So there is a kind of curiosity to have interaction with people of opposite sex. The conclusion of our talk is that gender ideology maintains oppositions for social functions. And what are those social functions? We will discuss them in the coming articles in detail. So far, our purpose was to understand how sexual dichotomy, gender difference, they are related with social functions and what do we mean by functionalism?


Hey guys, this is Nate, aka The Foot Account, and welcome back to the channel. A promo that brings some of the biggest names in Europe and new UCL heroes. Finally, it packs today. Team of the group stage. We got to take a look at what this promo looks like content-wise, SBCs, and evolutions today. Guys, we're going to go over it all and what to expect on the market. If you're excited for it, drop a thumbs up. Subscribe if you're new. Apologies about the voice. I literally feel amazing. I do not feel that sick anymore at all, but the voice sounds terrible, man, and it is frustrating. So we're going to get through it today. Try to keep it short. Let's go over yesterday's content. Of course, we had marquee matchups, but one thing that we were not expecting was a brand new Team of the Week player pick, and they made it cheap again. You can see 91% upvoted on this Team of the Week player pick. It is nice. It's cheap. 25,000 coins and 82 and 83 rated squad, which is very, very nice. Thank you, EA, for doing this. And of course, with that happening, you have Team of the Week cards on the market down from 40,000 coins. They are about, I believe, 30,000, 32,000 coins right now for Team of the Weeks. I wouldn't invest. I would not invest these cards right now. I know there's more SBCs coming today or refreshing today that require those Team of the Week cards, but I do not believe I would be investing in those just because that Team of the Week SBC is cheap once again. Now, we had marquee matchups as well, which did make a few cards move. So make sure you check your clubs, especially for some of those players that were required in MMS. But what I really want to look at is a leak, guys. There was a massive leak yesterday. Eusebio is coming as that Centurion's icon that we saw on EA's tweets earlier in the week. We knew we were getting a Centurion's icon. We finally know who it is. If Eusebio, that's a massive SBC. That is a massive card. A lot of people are comparing this and likening this SBC to the Jarzinio that we had last year in FIFA 23. Since that SBC was so early, it was such a meta card. People are looking at this in the same light, even though it's going to be more expensive than what that Jarzinio was. This could be a really insane SBC. Now, when's it going to drop and how much is it going to be? Since Eusebio on the market is around 3.5 million coins, or at least he was. He's been dropping, of course, because of this leak. I have to imagine that this Eusebio is going to end up costing around 3 million, somewhere around 3 million coins. And then I'm guessing it's going to drop on Saturday. But honestly, no clue. It could be today. It could be tomorrow. It could be Sunday. Any day in those three days, Saturday makes the most sense to me. But it was a really big leak. And that's an SBC that I think a lot of people would love to craft during Team of the Year, especially. This should be out for a long time. And during Team of the Year might be a really good time to craft this SBC for a four-star, five-star Centurion's icon card with absolutely insane stats. He's got the Rapid Plus play style. I know what I've never really used the Eusebio too much, but this SBC would tempt me to go after his card. If you have any aspirations of doing this SBC, it's probably worth your time thinking about stocking higher-rated fodder. We're talking the 88s and above, specifically the 89s and the 90s. Because as you've seen from Boppe and Zidane, they move the most for these big-time SBCs. And if it's going to be 3 million coins and expensive, it probably is going to move that higher-rated part of the market. So maybe you invest a little bit there this weekend if you're interested in doing Eusebio. Now, let's get to the promo that is at hand today. Team of the group stage, we've seen a leak, right? We saw players leaked early on in the week. We've got another full-team leak. This is from Foot Sheriff himself. It does include basically all of the same players. You've got Mboppe, Griezmann, Cancelo, Jude Bellingham, Rodrigo. Saka is in here. Rafael Leal, Musiala, Phil Foden, absolutely stacked team, guys. Big-name players. It's a nice-looking team. The question is, how packable are they going to be and how much does EA actually upgrade these cards? They need to upgrade these cards pretty good today. I mean, the question mark's going to lie within, especially for those players that already have promo cards, like this Bukayo Saka showing us 88-rated. His road to the knockouts is 91. What are they going to do to upgrade this card? Is it going to have a play style? I don't imagine that it'll be above 91-rated. It could be 90-rated or 88-rated, but will they give it a play style plus that will make it different and set it apart from the card that he already has in this game? That's where I'm really curious to see where EA will go with things today. They need play style pluses on a lot of these cards, because at least you think about even the most recent promos that we have had with Radioactive, the entire starting 11 of this Radioactive promo had play style pluses. Even the dynasties cards that are going out of packs, like so many of these top cards have play style pluses as well, that maybe their regular base cards did not have. We need to get those additions on a team of the group stage team today 100%. So again, this promo looks good because it looks like they're putting out some of the biggest, some of the best players. And yes, as we talked about earlier in the week, people are kind of not happy for some of the leaks because it's players that we've seen special cards for already this year. But again, this is a performance based promo and it is a promo based off of players who have performed well during the group stages of these European leagues. So that's the whole reason this promo is coming out. It is always a fan favorite promo and it's also going to be including what I think is a fan favorite, SBC. If you remember back a couple of years ago, we had a Harry Kane team of the group stage card. I remember using this card, it was absolutely insane. Harry Kane has leaked as an SBC or objective coming during this promo. Guys, to me, it's an SBC 100%. I don't think they would put Kane as an objective. They've done Kane SBCs before. This is an SBC that 100% I will be doing just because former Tottenham Hotspur player, big fan of the guy. But also he's English and he has a card here potentially that could have 80 pace. For the first time in this game, they could give him 80 pace. His team of the season card from last year, a lot of people remember, even his trailblazers card this year. The red one is very, very good with the power shot play style. Plus he's literally automatic scoring goals. And I think that this SBC will be a card a lot of people want to do. He's already got 95 shot, really good passing, dribbling, physical. It's the pace that needs to go to 80. So definitely keep an eye out for this SBC. I do believe he will be our SBC dropped today for 6 p.m. content. And they need to give him a good play style plus. They need to, like no more insights if pass EA, give us something good for this Harry Kane, boost his pace up to 80. And that could be a really, really dope card. Now for objectives, we got options. There's multiple leaks for objectives. We've got Di Lorenzo could be an SBC, could be an objective. We've got Griffo could be an SBC, could be an objective. Of course, we do have the Luis Alberto card as well. This is my take as who I think we'll see as our objective player today. I think it's gonna be Luis Alberto. So definitely keep an eye out for that. But that's the promo in total. Now let's talk about the heroes really quickly as well. These are all the heroes that have been added to the foot bin database. They're all actually inside of the game as well. We have not seen yet a Betty Pele, Milito. Some of these cards that have concept cards, but we actually haven't seen them from those pre-order hero UCL packs. So we're waiting to see how the pack weight on these are today. I can't believe it's gonna be that amazing. But that's another aspect of today's content that is gonna make things be pretty interesting. You've got new heroes in packs and they're promo heroes once again guys. So it's gonna be very similar to how we've had promo heroes and promo icons already this year, like the triple threat heroes that we have had. You think about like Ginola, Govoo and Cole, right? These cards, they have a different pack weight. They're not just the same pack weight as heroes. They will be a little bit more packable because they are special items, right? They're like promo cards. So I once again wanna point that out because that should hopefully make them a little bit more packable than regular heroes or like regular icons are because we all know those are very difficult to pack. So watch out for those today.  So they're gonna be great to trade with. They're gonna be awesome to fluctuate on the market. Schneider with his fives are five stars, gonna be nuts, a Betty Pele company, even the brand new Ramirez card. A lot of these are gonna be very hyped today. So we're gonna be keeping a close eye on those cards as well. Now we're still looking at evolutions that could potentially drop today as well. We're looking at the founders evolution like we talked about in yesterday's video. There is still a very high likelihood that this could drop based off of the leaks. But once again, I will say, we actually mentioned it in yesterday's video incorrectly. The founders evolution, the first one does not expire yet but we're waiting for this founders evo two to be dropped. There are two active evo leaks that we have by foot police. He also mentioned an all position evolution that has been added. He said it's different from the founders evo. So I don't know which one of these two is gonna be dropping today, but what I will say is like content Fridays on this game have been including evolutions in just about every single week. So I would not be surprised if some of those are going to be coming like this one's gonna be going away in a couple of days. Still have detail oriented that I'm working on there for a double evo, but looks like we're getting more evolutions, whether it's today, whether it's tomorrow, I'm not entirely sure, but Fridays have been a part of the normal content release. Evos have been a part of the normal Friday content so we will be watching evolutions very closely today and I'm super hopeful that it is that founders evolution because that would be super nice. Now let's talk market a bit, right? Because there were some decent trades to be had yesterday, but Fridays and weekends on this game are just not good for the market guys. They're really not. Like you can make coins with the brand new promo team that has been released and I think that's where you'll wanna be looking today for sure with the new UEFA men's and women's hero's cards that are dropped and of course the brand new promo team as well for team of the group stage, but guys prices over the weekends just drop and they're gonna continue to drop on a lot of different areas, even though there's weakening demand and people playing games at a very high rate because it's the weekend, the EA have just figured out a way this year to make the market drop on the weekends. In-packs card specifically, any in-packs inform gold card. So like Golden Boppa, well he's gonna be out of packs, Gold VVD will be in-packs, he's probably gonna drop, he dropped a lot last weekend, Gold Neymar dropped a lot last weekend, he's gonna drop some more, Puteas I would throw into that as well and then of course any current card in team of the week, even some in-packs icons and heroes, I definitely expect for that to be a part of the market that is dropping and even some of the out-of-packs cards. I'm not looking at these dynasties as a good investment because take a look at last week's radioactive team, how many of these cards are higher in price right now than what they were last week when they went out of packs. It's literally like two cards, Kante, Mazrawi, maybe three, if you include Zinchenko in there as well. Most of these cards have gone down and down considerable amounts since they have left packs like Sam Kurus, 400K, she was 578,000 coins last Friday. So big drop off there, I'm really not investing in this dynasties team. There could be, again, a couple cards that go up some, but I'm not expecting massive rises, so I would just be very careful in trying to invest in any of these cards. It's gonna be maybe one of the really rare, more meta ones that would go up or just a very, very popular card, maybe Hoyland, maybe Lorna Reese-James potentially, but make sure you get the club-stocked discard items. Make sure you get one of those in your club or at least a couple of them, especially for either an EVO or if EA do ever decide to require those in an SBC because there are so many that are discard. And watch out for Sane today as well with the links to the potential Harry Kane SBC. That'd be something to keep an eye on today as well. Now, also with the market, another reason why prices are probably gonna drop today, we've got a big SBC refresh in today. That is the 87 plus base or centurions icon upgrade and a lot of people really enjoy this SBC when it dropped on Sunday this last week. I think people are gonna get interested in doing this SBC again and it maybe moves a little bit of fodder. That's the next thing I wanna look at. Should you buy fodder today? Fridays are usually days where we don't buy too much fodder, especially when it comes to buying fodder for a market rise when everybody's gonna be opening packs for the new promo. That's just not something you usually wanna do or really ends up working out well. I don't think it's gonna work out that well today. If anything, maybe 86s and 87s would move up just a little bit. I think they moved the most last week on Sunday when this SBC came out. So I'll be very careful with that. You could also say Nate, what about team of the weeks because this SBC requires two of them and in the higher rated variety, right? 86, 87 rated squad, two team of the weeks, maybe, right? That'd be like the only place where I would think about risking it today but at the same time, guys, team of the weeks, I think they're just gonna keep dropping, right? We've got the player pick that is out. I don't see many great opportunities for a team of the weeks rising too much today. It may be an initial rise but then probably a drop after that as people get their rewards, those cards get supplied from lightning rounds. There's gonna be a lot of store packs once again today and there's even some new store packs that have been added to the code and updated in the code from what we looked at earlier on in yesterday's video and I wanna cover that right now. This is the premium and the regular Energizer pack which we saw during Black Friday as the 10 and 30 coin pack but EA changed the verbiage. Basically, these are the special 4U packs that we mentioned and that we saw earlier. That ultimate supporter pack that gives it two promo cards and a bunch of players rated 80 or above as well inside the pack. They changed it from saying on any available platform to log in, it only is for the game. It excludes the web and the companion app. So we're still waiting to see what these special 4U packs are. It sounds like it should be a free pack guys but it's actually not and then also there's a bunch of other team, the group stage packs that should be added to the store today on this game.  It comes down to the rarity of them, how packable they are and that'll really control how much hype there is today. But as you guys know as well, the quality of the player SPC, you think back to some of the more popular and hype promo Fridays of the year that coincides with some of the best player SPCs, right? Like when we had Rodrigo that was really, really hyped. I mean, there's been a couple of good weeks, it's been some bad weeks. So I think the cane SPC today or if it's somebody else, maybe that. But I think there's a lot of hype riding and potential hype riding on that SPC player today for the road to the knockouts promo. Now once again, I do in a stress, these are not live cards, they do not upgrade. These cards only are basically think of it as a team of the week for the group stages. That is what it is. It's a team of the week, one time static upgrade, kind of, I mean, showcasing those performances during the group stage and no longer are there going to be live Champions League cards. All of that is done. Same thing with Europa League and Conference League.  He's deserving of an upgrade. The Usman Dembele situation from the games that happened earlier in the week. He is due a 90 rated card as well. So those upgrades also should be happening today. We will see what EA do. And then the only other thing that we're still a bit curious about is what is EA doing with the UWCL RTTKs? We horror that has been leaked. Chowinga, Du4, Frygang, right? We took a look at some of those cards in yesterday's video. And again, we bring them up because we're just not sure how this is gonna work for road of the knockout cards. EA is gonna get people confused because these are still live. This card design is still a live card, a road of the knockout, still live for the women's Champions League. But everything else that's gonna be dropping today will not be live. So that's a bit of a confusing one. So we'll see how EA do that, whether those cards are impacts or if they are SBCs only. But that's gonna be the video for today, guys. Honestly, I hope it's a good day. I hope the cane SBC is sick. I'm really looking forward to that. Good luck if you're doing the icon SBC. We'll be streaming today, looking at all the brand new content and hopefully EA have some good stuff for us in evolutions and SBCs specifically. But if you enjoyed the video, drop a thumbs up on it. Comment down below if you have any questions and subscribe if you're new. Subscribe so I get my voice back. I don't know if that'll help, but we'll say that it will. So again, thank you guys for watching the video. It'll be Nate at the put account. Catch you guys later. Peace out.


Dr. Henry Osid Alright Michael, here's that video we made for the public, public image restoration campaign. Now I'm going to have to warn you, this video isn't going to be, well, easily understandable. Michael Handler. Wait, what does that mean? It's got to be good enough for the public, Henry, so it should be fairly easy to comprehend. Dr. Osid. Well, we wanted to make it more oriented towards a younger audience, so we tried to make it a little cooler, like with memes and slang and stuff. Mr. Handler. Oh god, I'll see. But you know, I of all people can't understand this generation any more than anyone else. Dr. Osid. Look, you have my office number, just give me a ring if anything stands out, alright? Mr. Handler. Fine, fine, see you later. Mr. Handler walks back into the PR office and takes a seat at his desk. He carefully inserts the flash drive Dr. Osid gave him into his computer and installs its contents. A single file called KIC-W-Y-H-D-A-T-S-C-P-F dot MP4 appears on his desktop, sighing he clicks on it and the video starts. An instrumental version of Never Gonna Give You Up by Ray Gasly begins playing as an animated SCP logo spirals into the center of the screen. The text, keeping it chill with your homies down at the SCP Foundation, sparkles into view around the logo. The screen fades into an outdoor setting with a heavily refuted brick wall in the background. Two men enter from either edge of the screen, wearing baggy lab coats over colorful t-shirts alongside athletic shorts, Nike socks and sneakers, and most notably generic blue baseball caps worn with the rims facing backwards. Dr. Chandler Wentworth was a party people. I'm Foundation researcher Chaz Wentworth, site director Douglas Robertson, and I'm his friend Bobby Doug, and together we are Dr. Robertson and Dr. Wentworth cross their arms and lean against each other facing out towards the camera and either side of the screen, both in unison. The Super Cool Peeps, a cartoon boing sound effect is audible. Dr. Wentworth, today Bobby Doug and I want to talk to you dope freaking meme lords about a lit organization made to keep all of you safe and able to go about your normal lives. Director Robertson, that's right Chaz, a lot of people have been saying some mean things about SCP that makes us very sad, but we want to tell you that it's all a bunch of yucky baloney. In fact, let me tell you guys a secret. The SCP Foundation has stopped the world from ending over 9,000 times. Dr. Wentworth, how crazy cool is that? It makes me want to, both in unison. Hit that da da da dab! Director Robertson and Dr. Wentworth each make their own shabby attempt at executing a popular dance move known as the dab, where in the performer points their flattened palms face down and out to one direction, allowing their outermost armpit to come in contact with the lowered face. Director Robertson, yate! Dr. Wentworth, yate! Mr. Handler poses the video, turning his swivel chair towards the phone. He dials Dr. Ozyd's office number. The phone rings three times before Dr. Ozyd picks up. Dr. Ozyd, hello this is Dr. Henry Ozyd, site 96 General Research and Containment. Mr. Handler. Hi Henry, it's Michael. Dr. Ozyd. Oh hey Michael, did you take a look at that video yet? Mr. Handler.  They just did like a dab or something? And now they're both going yeet or whatever that is? Isn't that a cuss word? Dr. Ozyd. I don't think so. Let me look it up. Dr. Ozyd can be heard typing something into his keyboard. Dr. Ozyd. Okay, I looked it up. It says yeet means to discard an item at high velocity. Mr. Handler. What? The Chandler and Douglas both just like said it randomly without any context? This has to be coherent if we want to release it to the public. Dr. Ozyd. Look, you know our intern Barry. He's 19. He knows what he's doing. He said kids these days just say yeet all the time by itself. So whatever, you know? Mr. Handler. All right, I trust you Henry. Talk to you soon. Dr. Ozyd. Bye Michael. Mr. Handler hangs up and unpauses the video. Director Robertson. But seriously squad, the SCP Foundation tries finna hard to make everyone on earth safer by containing dangerous or weird stuff. Dr. Wentworth. Of course that doesn't mean weird is a bad thing. But we wouldn't want something anomalous walking around would we? Director Robertson. Whoa there jazz. Ethic use of the word anomalous. Do you kids know what anomalous means? I could tell you myself. But I think I have a very good friend who would love to tell you instead. Come on in 999. Dr. Ozyd. Wearing a large foam costume reminiscent of SCP-999. Shambles on screen. Dr. Ozyd. Hiya kids. I'm SCP-999 and I love helping people. I want to help you by teaching you a big word. Anomalous. That's A-N-O-M-A-L-O-U-S. Something is anomalous when it does not follow the general rules of normal society. Now would a criminal be considered anomalous since they do not follow the rules? Certainly. However we're using it to mean something a little different. Here the word anomalous means something that doesn't make any sense. Like myself. I am anomalous because I am a living blob of slime. Dr. Wentworth. Wow that's a thick description 999. Thanks so much for coming. Dr. Ozyd.  In the process a large chunk of foam breaks off of the costume and lands on the floor. A cartoon bonk sound effect plays. He quickly bends down to remove it and falls forward. The same bonk sound is audible. Director Robertson helps him up and urges him out of the viewing area. Mr. Handler pauses the video. He places his forehead into his palm. And takes a series of deep breaths. After about a minute he sits back up and unpauses the video. Director Robertson. Now that you know what anomalous means let's talk a little about some of the anomalous things we have at the foundation. And why our work is so savage. I mean we could contain Slenderman. We could contain Dame de Cosita. We could even contain Big Chungus. Chaz what's your dopest favorite SCP? Dr. Wentworth. Bobby I know just a thing. Last Thursday I was in charge of a monthly event with SCP-4252. One of the resident anomalies here at Site 96. SCP-4252 is this wacky room full of, you won't believe this, baked beans. The room fills up with baked beans every month. And then we summon a magical bean leprechaun with a spell. And want to know the craziest part? He eats all the beans right there and then. If that isn't an our slash dank meme then I don't know what is. Director Robertson. Ah that's hot Chaz. Totally fire. But sadly fam a lot of the anomalies we keep are actually hecka mean. And that makes us feel nomi-gusta. Our biggest job is to keep you all safe from them. But for some reason haters keep trolling us by saying we are bad and we should destroy our company. And it just makes us want to do surgery on a grape. Dr. Wentworth. But here's the thing home slices. We actually do a ton of great things for humanity as a whole. We eradicated dysentery. Kept a fungal disaster from spreading. And we've trapped countless monsters who without our help would probably be going sicko mode on our tushies. Director Robertson. Now I think it's time we talk about the thing you all want to know about. And no it's not Fortnite. It's our very own D-Class boys. Here at the SCP Foundation we get help from our epic D-classes to test out the anomalous effects of various creatures and places that we want to know more about. In fact we have someone you might want to meet. Come on out. A man in an orange jumpsuit walks slowly on the screen and stands in between Director Robertson and Dr. Wentworth. He is visibly uncomfortable. D-411. Hi. I'm D-411 but you can call me Glenn. I used to be a bad guy.  But I got caught. I was in jail for four years before the SCP Foundation offered me a job. I've been here for like two weeks and it's better than jail. Other than taking a science test and talking to some old dude in the bathroom I've just been chilling. Dr. Wentworth. D-411.  D-411. I mean I guess. You know I'm still not free but at least I feel like I have a purpose you know. Director Robertson. Terrific. Well thanks for Netflix and chilling with us D-411. That was really swag of you. D-411. Yeah okay whatever. D-411 exits. Simultaneously a slight whistle is heard moving up and then back down. Mr. Handler pauses the video. He opens Internet Explorer and types the phrase Netflix and Chill into the search bar. After loading for 20 seconds a definition appears at the top of the browser. Netflix and Chill is an internet slang term used either as an invitation to watch Netflix together or as a euphemism for sexual activity either as part of a romantic partnership or as casual sex or as a groupie invitation. Mr. Handler shakes his head in disapproval. He scribbles something down on a sticky note before unpausing the video. Director Robertson. How the epic is that? D-Class personnel are actually treated great at the SCP Foundation. We take them out of prisons where they don't want to be and let them stay with us as long as they help with a few science experiments. Dr. Wentworth. And yes sometimes the experiments are dangerous but we do them because we want you dope peeps to be chilling on earth for a long long time and if we didn't have our D friends to test stuff for us we might not have enough information to keep everyone safe. A 1940s cartoon era Awuga sound effect is audible. Director Robertson. Well fam that's our cue to leave you guys alone. But I hope you dudes and dudettes keep everything we said in mind. And remember as long as you focus hard and get a lot done you too can save the world. Dr. Wentworth and Director Robertson wave at the screen as it fades to black. Mr. Handler takes a moment to unpack what he just watched. He reaches for the phone but stops himself turning back to his computer. He opens his email and checks the product deadline. Sunday April 22nd by 4 p.m. Mr. Handler looks at the bottom right corner of his screen. 3.42 p.m. 4.22. Mr. Handler sighs. He reaches under his desk and pulls out a half empty bottle of scotch and a glass. He pours himself a shot drinks it and presses send. And a file to learn more about the scp foundation subscribe to scp orientation today and turn the notification bell on so you don't miss any of our videos.


Time travel has been a topic of urban legends and Hollywood movies for as long as I remember. In fact, it appears that the possibility to travel through time, either into the future or into the past, has appealed to the imagination of mankind for centuries. While many may think it absurd to believe that we could travel back or forwards in time, some of the world's most brilliant minds have investigated whether it could already be a reality. From ancient India to ancient China and Egypt, there are countless accounts where the people involved have slipped years, sometimes hundreds of years, into the future. We have looked into ancient texts where we found a number of references to time traveling. You are about to witness some of these stories, so please sit back, open your mind and enjoy the possibilities that we have uncovered. The first story is of the Seven Sleepers. One of the first reports of time travel still has no real explanation and remains an authentic unexplained mystery. The story gives evidence that ancient civilizations may have had some knowledge of time travel. The story alleges that during the persecutions by Roman Emperor Deceus around 250 AD, seven young men were accused of following Christianity. They were given some time to recant their faith but chose instead to give their worldly goods to the poor and retire to a mountain cave to pray where they fell asleep. The emperor, seeing that their attitude toward paganism had not improved, ordered the mouth of the cave to be sealed. Deceus died in 251 and many years passed during which Christianity went from being persecuted to being the state religion of the Roman Empire. At some later time, usually given as during the reign of Theodosius II, 200 years later, the landowner decided to open up the sealed mouth of the cave thinking to use it as a cattle pen. He opened it and found the sleepers inside. They awoke, imagining that they had slept for only one day and sent one of their number to Ephesus to buy food with instructions to be careful lest the pagans recognize and seize him. Upon arriving in the city, this person was astonished to find buildings with crosses attached. The townspeople for their part were astounded to find a man trying to spend old coins from the reign of Deceus. The bishop was summoned to interview the sleepers. They told him their miracle story. When Emperor Theodosius II heard about the incident, he accepted it as evidence of resurrection and claimed the seven sleepers had in fact been dead the past 200 years. As the earliest versions of the legend spread from Ephesus, an early Christian catatum came to be associated with it, attracting scores of pilgrims. On the slopes of Mount Pion near Ephesus, the grotto of the seven sleepers with the ruins of the church built over it was excavated in 1927 to 1928. The excavation brought to light several hundred graves dated to the 5th and 6th centuries. Inscriptions dedicated to the seven sleepers were found on the walls of the church and in the graves. This grotto is still shown to tourists. The next authentic story of time travel is the story of Kakudmi, King of Kusastali. Kakudmi's daughter, Revati, was so beautiful and so accomplished that when she reached a marriageable age, Kakudmi, thinking no one upon earth was worthy of her, went to the creator himself, Lord Brahma, to seek his advice about a suitable husband for his daughter. When they arrived, Brahma was listening to a musical performance by the Gandavas, so they waited patiently until the performance was finished. Then Kakudmi bowed humbly, made his request and presented a short list of candidates. Brahma laughed loudly and explained that time runs differently on different planes of existence and that during the short time they had waited in Brahma Loka to see him, 27 Kaktura Yugas, a cycle of 4 Yugas, totaling 108 Yugas, had passed on earth. Brahma said to Kakudmi, O King, all those whom you may have decided within the core of your heart to accept as your son-in-law have died in the course of time. 27 Kaktura Yugas have already passed. Those upon whom you may have already decided are now gone and so are their sons, grandsons, and other descendants. You cannot even hear about their names. You must therefore bestow this virgin gem upon some other husband, for you are now alone and your friends, your ministers, servants, wives, kinsmen, armies, and treasuries have long since been swept away by the hand of time. King Kakudmi was overcome with astonishment and alarm at hearing this news. However, Brahma comforted him and added that Vishnu, the preserver, was currently incarnate on earth in the forms of Krishna and Balarama, and he recommended Balarama as a worthy husband for Revati. Kakudmi and Revati then returned to earth, which they regarded as having left only just a short while ago. They were shocked by the changes that had taken place. Not only had the landscape and environment changed, but over the intervening 27 Chachayugas and the cycles of human spiritual and cultural revolution, mankind was at a lower level of development than in their own time. The Bhagavata Purana describes that they found the race of men had become dwindled in stature, reduced in vigor, and enfeebled in intellect. Daughter and father found Balarama and proposed the marriage which was accepted. The marriage was then duly celebrated. These slips in time seemed very commonplace in the ancient world. I sometimes wonder that the ancient stories which we arrogantly classed as fantasy were part of a higher being which was present here on earth in the very distant past. I sometimes wonder if they would ever return or have they ever went away. One of Buddha's chief disciples, Kamura Kasapa, explains in the skeptic payasi that in the heaven of the 33 Devas, time passes at a different pace and people live much longer. In the period of our century, 100 years, only a single day, 24 hours, would have passed for them, he said. Another story comes from the Japanese legend of Orashima Daoro. One day, a young fisherman named Orashima Daoro is fishing when he notices a group of children torturing a small turtle. Daoro saves it and lets it go back to the sea. The next day, a huge turtle approaches him and tells him the small turtle he had saved is the daughter of the emperor of the sea, Ryuji, who wants to see him to thank him. The turtle magically gives Daoro gills and brings him to the bottom of the sea to the palace of the dragon god. There he meets the emperor and the small turtle, who is now a lovely princess. Her name was Otohime. On each of the four sides of the palace, it is a different season. Daoro stays there with Otohime for three days but soon wants to go back to his village and see his aging mother so he requests permission to leave. The princess says she is sorry to see him go but wishes him well and gives him a mysterious box called Tamatebako which will protect him from harm but which she tells him never to open. Daoro grabs the box, jumps on the back of the same turtle that had brought him here and soon is at the seashore. When he goes home, everything has changed. His home is gone, his mother has vanished and the people he knew are nowhere to be seen. He asks if anybody knows a man called Urashima Daoro. They answered that they had heard someone of that name had vanished at sea long ago. He discovers that 300 years have passed since the day he left for the bottom of the sea. Struck by grief, he absentmindedly opens the box the princess had given him from which bursts forth a cloud of white smoke. He is suddenly aged, his beard long and white and his back bent from the sea comes the sad sweet voice of the princess. I told you not to open that box and it was your old age. A shrine on the western coast of Tango Peninsula in northern Kyoto prefecture named Urashima Jinja contains an old document describing a man Urashimako who left his land in 478 AD and visited a land where people never die. He returned in 825 AD with a Tamatebako. Ten days later, he opened the box and a cloud of white smoke was released turning Urashimako into an old man. Later that year, upon hearing the story, Emperor Juna ordered Ono no Takamura to build a shrine to commemorate Urashimako's strange voyage and to house the Tamatebako and the spirit of Urashimako. Today, a statue of Urashimataro stands in Mitoyo Tagawa. Are these stories true interpretation of time travel? Albert Einstein concluded in his later years that the past, present and future all exist simultaneously and most are familiar with his well-known concept of relativity. That is, that time is relative and not absolute as Newton claimed. With the proper technology, such as a very fast spaceship, one person is able to experience several days while another person simultaneously experiences only a few hours or minutes. Yet the wisdom of Einstein's convictions had very little impact on cosmology or science in general. The majority of physicists have been slow to give up the ordinary assumptions we make about time. As Stephen Hawking once said, even if it turns out that time travel is impossible, it is important that we understand why it is impossible. We hope you enjoyed the video. Please thumbs up, share and check back in a few days for more from Kepler Telescope Channel. Thanks for watching.


We're in our body. I want that guy's knee on my belly button Okay, how many people are trying to sit up in the guy's legs bended and we're struggling there nothing wrong with that We can fix it. It's an easy fix. This is how we're gonna fix it. All right I've just got to curl my body under his leg more the other thing. I'm seeing is what we're handcuffing this arm again We're handcuffing this arm over and over So as I dive out we're doing a pretty good job of getting here, but I dive out to keep the legs straight I'm pulling y'all can't feel it, but I'm literally trying to pull his leg out of his hip hip socket right here now as I get to This position I curl my body under see how it twisted my body almost 90 degrees See how his knees sitting on my belly button So now when I sit up is the bend of his knee isn't a factor anymore See how it's nice and just sitting on my thigh right here Lot of you guys are a lot of you guys are just sitting like this Trapping yourselves again. I literally got to use the moment of my heel to curl my body under Okay, then as I curl my body under that left arm shoots under don't handcuff yourself here That's why I want you to grab with the sock with two hands Okay, don't try to hug it and just throw it around Control it all right heel comes to my butt when I control it top hand comes under and then I sit up Now I'm driving off this toe this guy's gonna hang on to this foot I'm not beating the best guys. I've got to get weight on my foot now Watch what happens when I drive off this toe, and I'm trying to get my chest over my foot Now this hand is looking for bottom shoulder if he hangs on too long step up shelf the leg What's staring me in the face right now? What's staring me in the face right now? Dependent on what kind of cradle you want to hit okay? But he's in a bad spot no matter what and if I have his bottom shoulder he can't pass my leg That's why I can step up right here a lot of people like well. I wouldn't step up He's gonna pass your leg pass my leg. Okay. He's in big trouble. That's why I cup bottom shoulder Okay, I want to make sure that that's super important because what's gonna happen is we're gonna get the scramble be rolling around I gotta find that window and duck it and I'm gonna show you a way to duck this leg It'd be like mad. So I double leg. He passes my leg Okay, and I'm gonna duck his ankle here double leg. He passes folk folk folk Right there All right, we get our timing down. We get our tendencies down. This guy kind of knows how to scramble Good job, bud. Still gonna end up on your back. So we got it But until we get to these positions and refine them and get real smooth with them It's gonna be a struggle. All right About two more minutes then we got our 15 minute break


Well, I'm sure by now you all think I'm completely completely completely crazy But it's totally okay because I like lecturing on things that very few other people like lecturing on a specially inter-observer agreement So keep in mind we're gonna look at inter-observer agreement You may have heard this is inter-rater reliability, but we're gonna beat that out of you today So why don't you just step right up and take your lashings? Seriously, like stand up here and I'm gonna again and again I'm really not a violent person but on these videos it sure sounds like I am why because these particular issues need beat into your heads not literally, but Man, sometimes I swear. I just digress anyway. So yeah, these issues are important and I don't want to ever hear you really refer to This as reliability. There's a reason and we'll get into that. Okay. Anyway Because it doesn't say anything about accuracy or reliability when two people agree that something happened That doesn't mean it's a reliable finding. It just means that they agreed There's a lot of problems with thinking that that's a measure of reliability the number one thinking maybe maybe it didn't really happen Maybe they're just like one person's trying to please the other person. Maybe it's just agreement, right?   I'm really impressed You're exetting a good example for the rest of the citizenry If that's a word anyway, so I'm digressing again. I apologize anyway So interobserver agreement says nothing about accuracy or reliability just about how much two people agree on something Which is really about believability, which if you ask me is equally as important as accuracy and reliability Why because you could have the most accurate data in the world You could have the most reliable data in the world and no one will believe you That's kind of sad, isn't it? I mean isn't that the state of science all the time? Science is always screaming about something. That's the most effective way to teach You should never feed your kids juice before say acts years of age or months of age You should never do this. You should never do that. I mean very accurate. It was like everybody's like yeah, but I don't believe you They don't believe them because they don't understand the methodology, but that's a different issue So my point being that blitted a bit there that not validity, but believability is drastically important as much So as accuracy or reliability, right? The method that you use to calculate in your observer Interobserver agreement produces different results folks This is one of those little bitches of science that it's like man. Dang it. Which method to use I don't know.  So anyway, here we go. Let's get some more The question really is this what is accurate enough? No, no, no, I mean did you have to agree 100% of the time you got a green 90% of the time You get a degree 75% of the time 80% 60% I don't know Everybody has a different rule. I've seen researchers and people use 90% of these people seem people use 85 I've been a part of a research team where we were forced to agree Which was really weird because I disagreed with people but yet we wouldn't leave the room until we agreed So finally we just got tired of staring each other spaces. We said fine. We agree it happened That isn't real agreement. That's forced agreement.  I don't like that at all And if you were the faculty member that made me do that, then you know that you're doing it wrong. Anyway Here we go.  We're gonna. Sorry.  Let me explain the graph before I explain all the numbers So we are gonna use an inter-observer agreement procedure called total agreement. So the count all right So we're gonna count the behaviors this is a Duration sort of setting or latency or whatever so we can use this in all sorts of different types for different types of behavior counts of behavior Durations of behavior latencies of behavior so counts how many times happened duration how long it happened forward latency How long from the stimulus the response right we got that out there So these are good for all sorts of things, but this particular procedure sucks Let me explain why so we have five minute intervals And we have the numbers of behaviors that occurred in each interval each row is a different person So row one is me row two is you all right. So row one I saw it happen to four three five zero zero three one three four You saw it three four two three zero two three two four five times So what do we need to do we need to add up each row? So your observes observations divided by my observations or whatever I don't know which ones highest and lowest so we take the smaller total divide it by the larger total look We agreed 86% of the time, but here's why this sucks. Look at the next set of numbers So again, five minute intervals. I saw it two four three five zero zero zero zero zero zero zero zero whatever you saw it zero zero zero zero zero zero zero three two four five notice that in no point except the middle two intervals Did we actually agree? However, I saw the behavior happen 14 times and you saw it happen 14 times. I'll be darned We saw it accurately a hundred percent of the time If you believe that I got a bridge for you man girl whoever you are The point is folks think about this We can set this up to where you didn't we were we and you and I didn't see anything at all the same ever But yet our formula says we did we agreed a hundred percent of the time.  Let's move on Exact agreement. These are nice ones. Okay, so this one's really really accurate again Same sort of scenario for measuring behavior types of behaviors Notice we're just looking at intervals where we agreed so the first interval is I saw it to you saw it three Right no agreement next one is an agreement because we both saw it four times next one three two no next one five three No next one zero zero. Hey, we agree next one zero two and then three three yay We get and then never for the rest so three out of ten is 30 All right now this was the same data set, right? So look at the data set now as the previous grammar backup. Okay, so look at this We're at the bottom line here, so we're back at total agreement It says we agreed a hundred percent of the time exact same data calculated differently using an exact agreement procedure It looks like this total number of agreements divided by the total number of our intervals Equals zero percent in that second set down there We did not agree at all at no point did we agree on our data and it represents that that's exact agreement So that's a exact agreement of occurrence. Anyway, there's other ways We can do exact agreement of non-occurrence and there's all sorts of layers here that if you were in a BCBA program You would get into but we're not so we won't Anyway, let's move on Interval agreement these are good when you're using partial or whole interval or momentary time sampling or just time sampling and Or the duration type behaviors, so we're using x's rather than numbers this time So we agreed in the first one not in the second not in the third yes in the fourth Yes in the fifth yes in the six not in the seven yes in the eight yes in the nine and not in the ten So six out of ten intervals we agreed in notice We have agreements of occurrence and agreements of non-occurrence here We could break this down again into agreements of occurrence versus agreements of non-occurrence And but we're not going to do that for the sake of this particular lecture actually we are Here we go This is what happens when you don't pre-plan and you don't read your lectures five seconds before you write before you record them Anyway, so this time we're going to only count the inner observer agreement for occurrence is the top half And in the bottom is the non-occurrence So we're only going to look at intervals that have x's in them if there are interval where we both were one or both Of us have an x okay, so the first one the second one the third one the fourth one not the fifth Not the sixth the seventh the eighth the ninth and the tenth right so that means we have one two three four Five six seven eight so four out of those eight intervals We agreed we agreed in interval one in interval four in interval eight and interval nine So we agreed about 50% of the time on occurrence If we do non-occurrence now we're looking at anything where one of us observed nothing right? So interval two interval three interval five and will six interval seven and interval ten So now there's six intervals, but we only agreed twice Which is intervals number five and number six because we both saw nothing So we only agreed 33% of the time and not occurrence So these are some very simple ways to calculate inner observer agreement remember This is not reliability, but this does speak to believability. So we want these numbers pretty high Historically for us to really trust our data. We want these above 85% Some people say 90 some people say 80 I picked an arbitrary number right in the middle 85% why because I don't know Some I probably read it in a paper somewhere sometime, you know 730,000 years ago Because I'm getting old and been doing this stuff for a while. Anyway, I don't really know There's no really set requirement here But a high number is it makes it harder to get but it also makes your data more believable This speaks to a bigger issue Which is that we should always have two people observing something at the same time independently to make sure we can get an accurate assessment of that behavior and I also understand because I work in the real world But that might not actually be possible. Anyway, we'll come back for more on something else later. Take care. See y'all