All right. Hello, everybody. Welcome to the February 2017 Wikimedia Research Showcase. Today we have two speakers. First up is Isaac Johnson, a third year PhD student at Northwestern University, who's going to talk about Wikipedia and the urban world divide. And then followed by Ellery Wilschen, a research scientist here at Wikimedia, who's going to talk about Wikipedia navigation vectors. So without further ado, welcome Isaac. Alrighty, let's get. Okay, so as Jonathan said, if you guys don't know me, I'm Isaac Johnson. I'm a third year PhD student at Northwestern University working with Dr. Brent Hecht. I'm going to talk to you guys today about Wikipedia and the urban world divide. Before I jump into things though, the work that that I'm talking about today is rooted in a paper that we presented at CHI last year. So in this talking on gloss over some of the methods and additional analysis we are running that paper, which I do think are interesting, but I want to have time for everything else. And obviously I'd love to check out the paper and be happy to answer other questions about some of the finer details. So a quick thanks to all of my collaborators, especially Aaron Hathaker and Wikimedia in general for allowing me to talk today. Now I'm going to assume that most of this audience knows what Wikipedia is, so I'm going to really jump into why we're interested in the urban world divide and how it pertains to Wikipedia. The rest of Wikipedia has been studied in the context of many dimensions of society in order to understand how well Wikipedia represents viewpoints as well as the notable information that are associated with these populations. So the role of gender has been studied by a number of people with efforts to increase the representation of women in the editor population and article space coming out of this. Likewise, we've had studies on differences and content between the different language editions. This is furthered kind of this goal of making Wikipedia even more multilingual. And there have been many other dimensions that have been studied as well. We turn our attention towards the urban rural divide. So what is the urban rural divide and why did we decide to focus our studies on this dimension. It is fundamental to peer production. The urban rural divide is it's a reference to the population density of people in an area, the distinction between the city and the countryside. How many people are actually available in a given area to contribute. For instance, if we look at the United States, here is a map that classifies each county in the United States based on one of six categories based on how urban or rural that county is. And so we look at just the urban areas. We have dark red being the most urban of counties sets counties like Cook County in Illinois, which is where Chicago and Evanston Northwestern University is. We also have the blues, which are the rural areas. You can see these comprised most of the country by area. And so how does this relate to Wikipedia though some foreshadowing of things to come but if you pull two examples from opposite sides of the spectrum. On one hand, we have Manhattan, which is in New York County. It's 1.6 million people over 3000 Wikipedia articles. And those are articles about actual places in that county. So it's things like Times Square, Central Park. And so, you know, that type of article. And we also have loving County in Texas, kind of on the opposite side of the spectrum, which is 82 people in the entire county and five Wikipedia articles still. And if we look at that in terms of densities, we're talking about Manhattan being way, way more dense. And so as many more people for each article in Manhattan at 530 people per article compared to loving County, which only has 16 people per article. So we're talking about very different settings under which content is being generated. But it's not just about population density. The urban rural divide also represents a strong cultural divide as well. A lot of you are likely aware of this. It's been in the news lately and will probably continue to do so for some time. So if we look back at our map of urban areas in the United States, this looks quite similar to the one of counties that were won by Hillary Clinton this past November. But these political and cultural boundaries aren't limited to the US. We see the same urban rural types of distinction distinctions in the UK around the Brexit vote this past summer in this case. The Washington Post who generated this map, they note similar patterns in France and Germany ahead of their elections as well as many other countries around the world. Now there's there's a long history here and I'm not going to delve too much further into it. But I just want to emphasize that the urban world divide isn't just about population density, which is say similar in ways to gender or different language communities. We're also thinking about urban and rural as populations who have different perspectives that might have different representations on Wikipedia as well. And for the final reason why we're interested in Wikipedia and this urban role divide. We've also seen evidence for this divide being important on other online platforms. So that's things like social media where we see lower participation rates in rural areas, as well as other peer production sites like open street map, where we see less accurate and information and lower coverage often. Now these points suggest that the urban role divide is both important and likely to affect Wikipedia, but no work had had really investigated how Wikipedia functions at at the different ends of the urban rural spectrum, which led us to ask the question that I'm going to address in this talk, which is, is Wikipedia equally successful at describing rural and urban areas. So our Wikipedia articles about urban areas systematically better than Wikipedia articles about rural places. And what I'm going to argue is that the urban role divide has led to uneven and particularly stubborn geographies of information. Now, in particular, that term uneven geographies. It's one that Mark Graham and some of his colleagues at Oxford and others have used to kind of describe these inequalities of information access and representation online. They oftentimes talk about this as between different countries. What they're arguing is that these online representations don't just reflect a place, but they also shape it. So these online representations actually have a real impact on the places they describe. And the other piece is this particularly stubborn part. And I think that's kind of what makes this research and what to do about it particularly interesting and different from some of the past work on other Wikipedia communities. So with that framing in mind, I'm going to move into how we address this broad question. So first things first, we needed a data set to study representation. So in particular, we needed geographic content. So there'd be a queer connection between a given article on Wikipedia and where it should fall in the urban rural spectrum. So we focused on English Wikipedia in this work, which led to almost 220,000 different geotagged articles. Again, where geotagged articles are a place that is or it's an article that has coordinates associated with it. So for example, if you have the article for Chicago, you'll notice that there's these latitude and longitude coordinates in the top right hand corner. And that indicates that's a geotagged article and where it is in the world. So we downloaded the complete history of all of these articles. And the next thing we needed to do was then label each article as urban or rural. And so kind of turning back to that map that I showed earlier, we took each article and we located it to the county that contained its coordinates. So we aggregated our data to the county level, of which there's over 3000 in the continuous US. So in this case, the article for Chicago, it will be assigned to Cook County in Illinois, like I said before. Just as a side note, there's articles like the one for the state for Illinois, which naively would be located to a single county in central Illinois, based upon its coordinates. But we were careful to throw throw those out before we did ran our analysis. And then we ran three different sets of analyses. So one focused on the quantity of content in urban and rural areas. One focused on the process by which that content was actually generated. And the final one focused on the resulting quality of that content. And our analysis for all three of these sections took the form of regressions. Now there's a lot to process here on this slide. So I'm not going to talk about everything. Again, the paper does have more details. But what's important is that the relationship between we're looking at the relationship between different measures of Wikipedia content in each county and how urban or rural those counties were. Now one thing I should mention is that we also had to correct for something known as spatial auto correlation. And that's this idea that we cannot treat geographies geographies being things like adjacent counties as independent in this type of analysis that they they're actually quite related. And so we use something known as spatial regressions and these control for the fact that the data points from each county are correlated with their neighbors. And if we'd skip this step, what would have happened was we would have ended up with some overconfident p value estimates in our regressions. So that's the that's the main effect there. But with that, I'm going to jump in and to what we found. So what you're looking at right now is a map of the number of articles per capita by county. That is, if you if you had a county with 10 wikip geotag Wikipedia articles and there were 100 people in that county that would be point one article per person. And before we get too further into into what it's showing you want to explain a few things quickly, because you'll see a lot of these maps as we go through the presentation. So those numbers on the bottom left, the minimum and maximum that maximum values from the data, the color scale itself, in this case is log transform, as indicated in the bottom right hand corner as well. So the lowest values on this map of the dark reds, the highest values of the dark blue. Vector map. There's three largely rural areas of the country that I'm going to point out now. I asked that you don't get too hung up on my very precisely drawn boundaries though. So in the rural upper Midwest, we're seeing lots of blues which indicates many articles per person. And you see the same thing in rural Appalachia for the most part as well. But then down in the south which is which is quite rural. We're actually seeing a lot of reds which indicates that we're pretty low on articles per capita. And this is where some of the other social and economic status variables would enter the picture. And also we have cities like Chicago which also tend to be read showing that they have low rates of articles compared to the more rural regions surrounding them. And so when we run our spatial aggressions we do find a significant negative correlation, indicating that more urban areas are associated with lower amounts of articles per capita. So if you look at that coefficient something that's a little more interpretable, a little more interesting. The most urban areas have three times less articles per capita than the most rural areas. And that's controlling for our socioeconomic status variables which kind of glossed over a bit of things like median age or household income. So it's not bad that rural areas actually have more content per person. And this actually does make some sense in the context of Wikipedia. A rural town of 200 would get an article, but city blocks of 10,000, you know, city blocks of many more people than that do not get their own article. And this result is consistent as well. If we look at not the actual articles per capita but say the total length of articles in a county, we see similar numbers, which begs the question, where did all this content come from? Our rural areas really producing content at higher rates, despite everything we know about their lower technology adoption and online presence. And to understand this, I want to talk about Ortana, Pennsylvania, which is a small town in South Central Pennsylvania where I happen to grow up. So this is the Wikipedia article for Ortana, Pennsylvania, and it's got two sections, the geography section, the demographics. And so how did these sections get to be there? If we look at the edit history for this article, we can actually go through and divide these editors into various groups. And we did this based on some really excellent prior work by Stuart Geiger and others in this domain. So we have users like Sportbot. Sportbot's a bot, or it's an automated agent. In this case, it substituted a template in the article, it seems. But bots also often add structured content, such as census data. And so bots fall under automatic editors in our analyses. In the edit right before Sportbot's was H-Mains. This is a person with a user account. If you look at closely at the edit summary though, you'll see that H-Mains used AWB, which stands for Auto Wiki Browser. It's a semi-automated editor, and it allows users to make lots of very repetitive edits very quickly. So we put that in a semi-automated category. It's not automatic like bots, but it's also likely not leading to adding of very complex content either. Further down the history, we have these three edits from anonymous users. We don't know too much about them, but they are manual human edits. And then we have Ken Gallagher. This is another person with a user account, but no Auto Wiki Browser this time, which would seem to be a manual edit. But we did make one final distinction though. And that's that some users seem to indicate an interest in an area by spending a large part of their time on Wikipedia adding articles about places in that area. We're going to call these editors locally focused. But Ken is not a locally focused editor in Adams County, which is where Ortana is at. In fact, no editors on the page for Ortana indicated a local focus in Adams County. For this research, Ken would have been considered locally focused if he had made at least 10% of his spatial article edits in that county. So we call Ken a fly by editor in this case. There's some qualitative coding of content that was produced by each group of these editors. We actually determined the portion of bot-like content. So that's things like census data or syntax. That is generated by fly by editors is 40%. And for locally focused editors, it's actually about 16%. Which is to say that locally focused editors tend to be producing more complex and interesting content in these areas. So with these editor groups in place, we parse the edit history of each article for all the places in the United States and track to generate what content. And we use some excellent tools here developed by Aaron Housaker. So thank you for that. So what were the results? Well, both the percentage of human content, that's content that's not generated by bots or auto wiki browser edits, as well as the percentage of content that's generated by locally focused editors shows a strong urban bias. And what this reflects is that the proportion of human generated content in the most urban areas is 95%, while in the most rural areas it's 78%. It means that a lot higher proportion of content in rural areas is coming from bots and bot-like editing. And if we go a step further to just the locally focused editors, we see that the proportion of locally generated content in the most urban areas is 38%, while in the most rural areas it is 4%. So those editors, the ones that are producing the more interesting edits, they're also quite scarce in these rural areas. And when we map out human generated content across the United States, what we see is this. So in this case, red is lower proportions of human generated content. And looking at the range of values, there's a large patch of the country in the upper Midwest where the Wikipedia content is less than 50% human generated, which means it's over 50% bot or auto wiki browser generated. And this lower proportion of human generated content in rural areas leads us to our final question, which is what is the quality of rural peer production information? There seems to be relatively lots of it in the US, but it's less often coming from focused editors that demonstrate a specific interest in the area. Now one way to measure this is through the Wikipedia quality assessment scale. Aaron talked in depth about this back in December about the scale and some of the more nuanced ways of using it. We did not go that deep into it. Though I will say Aaron's talk to give me, you know, really thinking about some cool additional analysis that we could be running. But the important piece here is that though it's not perfect, it's been shown to be quite useful this quality scale. And so most articles have been judged to be at the low end of the scale, stubs and starts. And what we did in our analysis is we determined the proportion of articles for each county that were rated as at least C class. So that's the first level where the article is said to be useful to a casual reader. And I'll say up front that a question that often arises at this stage is whether or not we should really expect a lot of these rural articles to be of high quality. And I think it's a legitimate question, something I'm happy to hear people's opinions on about later. What I'll say now is this though, there are examples of some really good featured articles in rural areas. For instance, many covered bridges in Pennsylvania have reached this status. And I'm going to try to address this idea of quality in rural areas a little bit more later as well. But getting into our results, what we saw was that indeed the quality of articles in urban areas is greater. So what the spatial regressions in this case are reflecting is that while about 10% of articles in the most rural or the most urban areas are at C class are greater. Most rural counties don't even have a single C class or greater article. And if we look back at the article from Ortana, this isn't particularly surprising. So it's largely a template in which census data has been inserted. And that has resulted in a fair bit of content for the 173 people. But it's been generated by bots and semi automated tools. And as a result, quality has not been judged to be particularly high. Now this is largely because that there is there's a much higher burden on rural residents to produce produce content. Whereas if you think about Chicago, Chicago can pull from many people to write about their history or the geology of the land. You know, there's many people in the area who might be interested in writing those pieces. But rural areas and many fewer people available would be expected to produce that content. And so we look at the whole country that we do see some really interesting pockets of higher quality articles. So states like New Jersey, Oregon, Oklahoma, they've really clearly organized towards producing higher quality articles. I think these are through their wiki projects in many cases. And this is very promising. It does show that achieving high quality articles in these rural areas is possible. But we still see very low quality in general across the major rural areas of the country. And the recap before we get into really talking about what all this means. What we found is that there's more content per person in rural areas. But the content generation by humans is much higher in urban areas. And that has left us with higher quality content in urban areas as well. So what does this all mean? Peer production is less like peer production in rural areas, which is to say that peer productions we kind of generally can see that this crowd sourcing process does not seem to be happening in many rural parts of the country. We instead see that the content in these areas is much more likely to be bot-generated. And it's largely governmental information that's been reformatted. And I want to say that there's, in general, I think two perspectives we can really take about this. One is the kind of, you know, maybe more negative one, which is that we failed to produce quality content about many regions of the world. What I think is getting lost there, though, is that producing content about rural areas is actually much harder. As I said before, given how many fewer people live in these areas and the fact that they still have to describe a large number of places and things like the geology of the area that aren't necessarily tied to population. So instead to prefer to kind of focus on the fact that Wikipedia really is the sole accessible online source about many of these areas, which means it can really do a lot of good as far as moving forward towards providing access to information. So kind of along these lines. While bots are not great for quality, I think rural areas do need them. So if we kind of take a step back now at this point, if we were to choose a country that's most likely to have high quality content for our areas, it's probably the US. There's a, you know, kind of early history of agrarian ideals and there's these very active governmental data collection agencies. But this isn't true of many countries. So like specifically in this study actually we looked at China as well in the Chinese language edition of Wikipedia that's there, which I know is a it's a complex situation, but it provides an interesting comparison nonetheless. And so here is a map of China working at the population by prefecture now, which is kind of a county equivalent there. And in this case, the West is the most rural region of China. And if we look at articles per capita now, which is was our basic quantity metric, you'll notice that the map looks pretty similar to the map of population density that is more rural regions are still bereft of content. And if you remember back in the US we actually saw higher articles per capita in rural areas, which suggests that there was a fair bit of content if not, you know, the highest quality. Here we don't even see much content begin with though, which leads to the next point. We need better bots if we want pure production to describe rural areas worldwide.  But I think one possibility is really thinking about new structured data sources that maybe reflect things that are more important to rural areas. So for instance, maybe New York City doesn't care how much arable land the US DA says they have, but that's pretty important to a lot of these rural areas. We might also consider if there's more ways that we can tie these rural areas to their larger region. So right now there's a lot of templates aimed at that. But for instance, if no one has written the article about the geology or sorry, not the article, but the sections about the geology or the history of a given place, especially in a rural area. Can we still link that article to the geology or the history of the broader area. And another aspect of this that I've been thinking about a lot and kind of actually started some exploration into is how the imbalances in data about rural areas might also affect the algorithms. So beside the kind of obvious human audience for these articles, which is actually quite sizable in many cases when we're looking at these articles or these counties, the median I think rural county had something like 6000 page views per month. So there are still a lot of page views going towards these, but they're also being consumed by, you know, quote unquote intelligent technologies. So this is kind of maybe a nice segue into our, you know, an early segue into our next talk, because I think that there's going to be some overlap when I'm about to speak about. But if you convert an article into a vector embedding of information that you know a machine learning model can really understand. The question is, you know, do rural areas which kind of all look the same because the articles are in many cases are based on the same basic template, just with different values inserted. What happens to how these articles are understood by these models. You have just wanted to let you know. Okay, thank you. I'm wrapping up now, but finally getting back to this idea of whether better rural articles are important. Again, the fact that there are lots of articles out there about rural places in the US is already pretty cool. I think the big questions invocations really start to emerge when we talk about other countries that don't have open and high quality structured data available. This is also where Wikipedia is really so important because it's the online source of information in many cases. Another original question is Wikipedia equally successful at describing rural and urban areas. I think the answer is not yet, though we do have some quantity in some areas, the quality is not there yet. But I really think that there's some some interesting paths forward in this area. And with that, I'd like to thank everybody who helped with this work, as long as many, you know, which is a good number. And I'd be interested to hear thoughts and questions now. All right, thank you very much. I'm from IRC, Aaron. You're muted. Or muted. There we go. Okay, so there aren't any questions, but there are a few comments that came up that I think it would be beneficial to to relay. And so small bones says that the featured articles uncovered bridges in Pennsylvania are almost all from one user, which looks like it's pronounced for a fish. But I'm not quite sure how to pronounce that. That's actually I think there's a similar situation. I think it was Indiana where you see the same thing where somebody has decided that they really want to improve a lot of the articles in that area and you've seen the same kind of increase, which is I think that's a really it's a really cool point because it shows that one person can really have a large impact on improving the quality in in these areas. But I guess, you know, about 10 or 15 years into Wikipedia and we still see a lot of these areas that haven't had that. And so that's maybe why we're thinking more about all right. So rather than relying maybe on some of the on some of the human interventions are there are there other ways that we could be generating quality content in these areas. So, and then I have a question myself which kind of riffs on this idea of importance. So, there's tools like suggest bot and and bots that produce lists of articles for editors to work on who try to organize lists of articles by the biggest potential impact that you can have. Like splitting articles by quality importance to see, you know, like how much of an impact your contributions might have there. It seems like this this idea of, you know, rural gaps might change that notion of where where your biggest potential impact is. I'm wondering if you can tell me what your thoughts are on that. Yeah, I actually have thought a little bit more about this, especially kind of in, in the context of what I was saying about these locally focused editors, because if you, if you think about, you know, how would you, you know, how would somebody add content to a rural area. And in many cases, the sources of information about these areas aren't really available online. So you'd have to kind of be going back through, maybe in your graph newspaper articles or things that a little harder to get at. And so maybe it's harder to pull in, you know, an individual, you know, a random individual, even if there is a potential for kind of high impact. You know, we've seen a lot of people looking at this area, but we don't have very high quality information and be great if we increased it. I don't think in this case, in many cases, it's not just any given dedicated individual can do it. And oftentimes it has to be somebody kind of located nearby to this county or maybe who knows what is missing. Because that's also a large part of the problem in many cases. And so maybe incorporating some, you know, for instance, with anonymous users, if you note, you know, based upon their IP address, they seem to be from somewhere somewhere in here or based on prior edit history for somebody who's a user. They seem to be interested in that general area. There might be maybe options to kind of try to preferentially or, you know, give focus on trying to get them to add content if we decide that there is really a clear need for better content in that given area. Cool. So there's a couple of questions that showed up on IRC. Well, I was asking that question, but I should ask JMO how we're doing on time. Let's get one more question in. Okay. So this is another one from Small Bones. So is there a difference between places that are close to urban areas? For example, 10 miles, 100 miles, 200 miles population within those areas? Yeah. So in this presentation, kind of in an effort to keep it a little simpler, we've really focused on this kind of binary distinction, which obviously isn't totally valid. There's a lot of nuances in that. And so what we've seen is that really where you're seeing the highest quality content isn't the most urban areas. It's kind of like a small step back from that. So oftentimes it's the ring around the city, you know, some of the suburban areas, which maybe is reflecting other demographics that are geographically correlated. But what ends up happening is that oftentimes you essentially see the suburbs with the highest quality content or maybe some of the smaller cities that are still relatively large. And then urban areas. And then it's, yeah, when you really start to get into the, into kind of the much more rural areas, the much more remote areas, that's where you really see the drop off. Obviously there's things like national parks where you see an increase again, even though it's rural, you see a lot of content we control for those are analyses and really didn't see any difference in the numbers. So, yeah, that's an interesting point though. So I appreciate that. Thanks, Isaac. So there's a lot more discussion happening in IRC. I'll drop you a link via instant message so that you can hop on there and talk to these folks about what the questions they have about your research. All right, fantastic. Thank you so much. Awesome. Thank you very much, Isaac. Next up, we have Ellery Wilson, who is going to talk about Wikipedia navigation vectors. So take it, Ellery. Jonathan, can you all hear me okay? Great. Yep, loud and clear. Okay. So this will be a relatively short talk covering some exploratory work I did last year on learning how to construct vector representations of Wikipedia articles, not from text but actually from data produced by readers while they're navigating Wikipedia. I'll start by motivating the work and then describe the methodology and discuss a few applications. So when building different machine learning services or recommender systems for Wikipedia, we often need a numeric representation or vector representation of all the articles in a Wikipedia. And then a learning task for which we might want to use these vectors could be training a softmax classifier to predict the importance class of an article.  And the article text is usually very rich in information, so these approaches can work quite well. But in addition to article text, I should also add that people in addition to text people have used the link structure to also generate vector representations of articles, especially in sort of multi-lingual applications. With article text and links, we also have a very detailed record of how articles are consumed by people navigating Wikipedia. And when people surf Wikipedia, they're using a diverse array of signals including article text, links, third-party search engines, and their own domain knowledge to determine what to read next. So this talk is about how we can use this wealth of behavioral data to generate very different and potentially complementary article representations. Another advantage that I'll discuss is that using navigation traces in conjunction with Wikidata, we can create a single representation for an article across all languages, and don't have to deal with sort of 200 plus languages and models when working on multi-lingual applications. So, yeah, all the work is kind of based on an algorithm called Word2Vec, and Word2Vec is a popular supervised method that was originally proposed for generating vector representations of words given large amounts of text. And so in short, kind of like what this work is about is applying Word2Vec to sequences of articles within reading sessions instead of sequences of words within sentences, which is sort of the natural use case. I'm going to explain – I'm actually not going to explain the full details of the algorithm here, and there's a lot of really great resources online on the technical details. But to give those of you who are unfamiliar with the method, I will give a high-level overview of one of the variants of Word2Vec to build intuition. So, in the SkipGram version of Word2Vec, we're basically creating a model for the probability of a so-called outside word denoted as O occurring near some other word that we're calling a center word within a sentence. So, C is a particular word in the sentence, and O is some other word occurring near C, say it's left neighbor. And so this probability model is parameterized by two matrices, I'll call them U and V, where each matrix has a row for every word in the dictionary. These matrices are initialized randomly, but modified during learning in such a way as to get a good estimate of the probability of word O occurring near the center word C. And after fitting this model, we can use the rows of U and V as our vector representations of words. So, these vector representations are kind of an artifact of training this probability model. So, like I said, although Word2Vec models were developed to learn word representations from a corpus of sentences, they can really be applied to any kind of sequential data. And these learned representations have the property that items with similar neighbors in the training corpus have similar vector representations. And by similar, I mean as measured by something like the cosine similarity or the Euclidean distance, it doesn't really matter. So, as a result, applying Word2Vec to articles in reading sessions results in article representations where articles that tend to be read in closed succession have similar representations. And since people usually generate sequences of semantically related articles while reading, these embeddings also capture semantic similarity between articles. The vector representations that we get from Word2Vec tend to be very high dimensional. I mean that's a choice, but we generally need a lot of dimensions to be able to get powerful representations. But we can reduce the dimensionality of our vectors using methods like PCA or TSNE to gain intuition for the relationships between articles that are encoded in the embedding. So this is a TSNE projection, so we're projecting 200 dimensional articles down into two dimensions of the 20 most popular English Wikipedia articles for the first week of February 2016. And what the graph shows is that there are, it's not very pretty, but there's several distinct clusters of related articles. So in the bottom right, there's a group of articles about the Super Bowl, right, you have Ken Newton and Peyton Manning, I think these are the quarterbacks that were like the superstars in the Super Bowl that year. In the center right, there's a cluster about the presidential primaries, so there's Marco Rubio, Ted Cruz, Bernie Sanders, the Iowa Caucus. And then in the top left, there are articles on topics that might appear in the tabloid. So Donald Trump is like between the tabloid cluster and the presidential primary cluster, which is interesting. Okay, so like I said, I alluded to this earlier, but we can actually learn representations for Wiki data items by simply mapping article titles within each reading session to their corresponding Wiki data items using Wiki data site links. These Wiki data vectors are then jointly trained over reading sessions for all Wikipedia language editions, allowing the model to learn from people across the globe, not just the readers of an individual language version of Wikipedia. One advantage of the approach is that it overcomes data sparsity issues for smaller Wikipedia's. There's a lot of data to train these word to back models. And the reasons that the representations for articles and smaller Wikipedia's are shared across many other potentially larger Wikipedia's as well. And finally, instead of needing to generate a separate embedding for each Wikipedia and each language. There's a single model that gives a vector representation for any article in any language provide the article has been mapped to some wiki data items. Okay, so that I've given you an overview of the algorithms and the methodology. I'll give you an overview of how the training data is generated from the raw web request logs. So, we start by taking one month's worth of requests to all Wikipedia's. And then do sort of one level of redirect resolution we don't really want representation for redirects we want representations for the articles they redirect to. And then after doing that there's a couple filtering steps so requests for non means main name space articles removed. Do my best to remove disambiguation pages doesn't work that well. Remove request for articles that were requested by fewer than 50 clients. And I also remove requests from any client who made an edit. The last two points are they don't help the algorithm they're just sort of security requirements for releasing the data publicly at the end. So after these preprocessing steps I group the remaining requests into sessions. And to build sessions requests are first group by client at the foundation we actually don't have unique tokens so client is just identified by an IP and a user agent. And so a client sequence of requests is broken into a new session whenever there's a gap of 30 minutes or more between requests. So this is how we, you know, group requests by client break them to sessions and then I also do a bit of session processing so sessions with the request the main namespace are dropped because the main namespace somehow ties may disparate articles together. In a way that I observe reduce the quality of the embedding so if there's a main namespace request in the session that session is dropped. Also consecutive requests for the same article within a session are collapsing to a single request this is quite a common pattern and sessions that are too short too long are also removed. Okay, so after all this processing. Generally, we get sort of 370 million sessions from a month that contain about 1.5 billion pages for articles. Okay. Another sort of fine point in the methodology so work to back has many many different hyper parameters to tune some of which sort of critically impact the quality of the resulting representations. And I won't go into model tuning methodology right now. Maybe if there's time we can talk about it during the Q&A, but it's documented on our research page that I've linked from the slides. Okay, so now that you have an overview of the methodology might be asking yourself how well this approach actually works. And this could be a tricky thing to evaluate, but I'll let you form your own judgments I developed a simple demo application that uses the article representations we learned to generate sets of related articles. So, you know, behind the scenes what's going on is that given an article, we first look up this representation and then find the top K articles with the most similar representation as measured by say the cosine similarity. And so feel free to check out the tool yourself it's currently hosted on labs, but I'll go through some examples that I prepared. Okay, so here are the most similar articles, according to our model to the article on Martin Luther King, Jr. You can see in this list, these are the top eight related articles you see many important civil rights leaders as well as some important events in this life. Here are the most similar articles to Germany. Again, we see articles on large countries bordering Germany as well as articles on sort of high level German geography and demographics. Here are the most similar articles to the Apple article. So what's interesting here, the model returns various popular fruits in terms of pages. And, you know, in contrast, a text based model here is much more likely to return a list of different Apple species. So this is like the first time you can see qualitative difference between these navigation vectors and text based vectors. So here's an even more stark example. Here is a query. The query article is Francis Patrick Donovan, sort of, you know, maybe an obscure Australian diplomat I don't know how famous he is. But you know, just based on the titles here, you know they're unfortunately there aren't images, the results, like look a bit odd, right. You maybe see articles on other Australian diplomats or, you know, the Australian Foreign Service or something. But if you dig a little deeper behind all of these recommendations what you see is that all these articles have a common connection to Rawl Dahl or one of his children. Yeah, so while text based model rate return results for other Australian diplomats, our model returns articles with a common driver of traffic, namely Rawl Dahl. So again, these representations can be quite different from text and hopefully in a way that's complementary once we build applications. Okay, so in addition to the demo, the article representations can be used in various different applications. So first they can be used as powerful off the shelf features for various machine learning tasks involving Wikipedia articles, such as modeling how many you know trying to predict how many pages an article will get based on the the article vectors are trying to predict importance classes I mentioned earlier. You could also use these vectors for building data visualizations and making sort of maps or alternative interfaces for browsing Wikipedia. And then there's, you know, lots of applications around the recommender systems so the reading team at the Wikimedia Foundation recently introduced a related pages feature it's currently in beta. I think still that gives readers through recommendations for further reading and the current recommendations are based by based on the more like this query feature in elastic search, but instead we could generate recommendations for further reading by looking up the nearest neighbors in the of the current article the readers on in the embedding similar to the demo. Further application might be link recommendation. So if articles are frequently read within the same session you might be able to make Wikipedia easier to navigate if you were to create a link between them. So for a given article you could generate recommendations for links to add by finding the nearest neighbors that are not already linked and adding a link if the original article has a suitable anchor text. Again the wiki data embedding would allow you to build a model for all languages. Okay, so yeah, future work. There's developing the application that just mentioned. There's getting a better understanding of the difference between representations learned from text, and the representations learned from readers right now. This has been sort of ad hoc qualitative work. Jonathan Morgan the foundation is working on a more rigorous qualitative study comparing the more like search with recommendations generated by these embeddings, which will hopefully give us a deeper understanding of how how these embeddings differ and what people actually prefer. And then I'm also interested in comparing where to vet representations to representations that are learned resulting from training an LSTM on reading sessions. Yeah, so in terms of resources I've been putting the embeddings that embedding is the term for article representations for every article in Wikipedia together so I've been putting these embeddings up on figs here. For the last couple months. I'm really relying, you know, you can look at the code it's on GitHub but sort of the core algorithm is just I'm just using Nicholas original see implementation. And then you can, you know, read up on more information on our research project page and join the discussion or ask questions if you'd like. But yeah, for now that's, that's all I have and I'm happy to take questions. Thank you very much Ellery. All right. Any questions from IRC. So, okay, good. I was just about to say I don't have one and then one appeared. So, so this is we've been we've been experimenting with in IRC with trying this out with some fun articles. And a lot of the articles we're getting this error seed item is not in the embedding or no neighbors above threshold. What does that mean. Okay, so that means. There's a version that's up online right. There's a couple reasons why an article wouldn't be in the embedding. And that is, if it doesn't have a corresponding wiki data item. Then for the demo that's up, it wouldn't be, it wouldn't be there. If it hadn't been viewed at least it would be separate clients in the month that we built the embedding for privacy reasons it wouldn't be included in the embedding. And then I could have a bug in my implementation. But yeah, basically, the, I think the current the embedding that's up there should only have, you know, would only produce recommendations for roughly two thirds of articles on say English Wikipedia, just based on the traffic patterns during the month that it was trained on. But yeah, there might be something else. So, yeah, if you could just send me those articles, I'll take a look. Okay, so there's one more from Eric murder and son, which is one interesting thing that can be done with word to back is the relationship between things, such as the semi famous example of king minus man plus woman is approximately equal to queen. Any investigation of similar relationships found with article to back. I haven't done, I haven't tested any of the analogies that, you know, word to back is famous for but Eric if you have, if you want to propose one, I mean what we could do is we could try the some of the classic examples from word to back because there is, you know, a queen article a king article, you know, maybe a male and female article could see what happened. I have not investigated it. And that's kind of a really common way to evaluate the embeddings to see how well they do in these analogy tasks. When I tune the model I do something different because what I care about is, I try to, I try to basically use the model to solve a prediction task for what, you know, given what you read, can I predict the other articles that were read in that session as well. So what I do is. This is how I do the tuning so take a random article from a session. And then query the embedding for nearest neighbors. Find the positions of all the other articles in the session and then compute the mean reciprocal rank, and then do this over a bunch of different sessions. And this is how I'm evaluating the embeddings. So you can think of it as like how well given one article can you predict what other things people will read. And compared to unfair because the, you know, the word to make models trained on this data but compared to say the more like query, the mean reciprocal rank of the best word to make models like 0.16 and the more like queries around 0.9. So they're very different in terms of their ability to predict what else people are reading. So I don't see any other questions coming up online. So I'm going to ask one of my own, which is so so it seems like this article similarity thing might be generally useful using it to help people, you know, know what might be interesting to read next is one potential use case. I wonder if you can talk to us about other interesting use cases that you you would like to explore or maybe that you'd like to see other people explore. Yeah, the, it gives kind of this just like very generic implementation of a notion of related articles so I think for any kind of task recommendation. This could be really useful. One example right is, you know, we're there's I don't know if you guys know but there's this suggestions feature in the translation recommendation tool, and there, you know, something could be like oh, you know, for an editor we can model their interests by say taking the average of the vector representations of the articles that they've edited recently or, you know, added the most bites to whatever so then we get like this vector representation of their interest. Okay, and then you say oh, okay well, you know I can use wiki to define all the articles that are missing in the languages that you edit in and then I can rank them by how close they are to your introspector like that would be one thing to do and I think yeah. I brief you talk about links there's reading this translation but in general, sort of. I think it's a really rich domain for for task recommendations, but that being said, if you, you know, alternative methods based trained on text would all are also great for task recommendation right so I think this is kind of particularly good potentially for for readers because it is trained on, you know, the data that they provide. Cool. Any other questions. I have one more I guess. So I'm like, I guess I'd just like to hear a little more. One of the one of the interesting things I think about the potential value of the of the wiki vectors model is the ability to infer relationships on article relationships on one wiki. Assuming that they're you know wiki data common wiki data items. Could you talk a little bit about the potential kind of benefits and drawbacks of this model implemented on a much smaller wiki than English Wikipedia. Yeah. So there's, there's two things you could, you could try to train a model, just on the traffic to that Wikipedia, and not do any of the wiki data mapping. There, you know, if the wiki is smaller, one advantage over text would be that maybe the article's text isn't very well developed yet, but people may still be browsing it, and you can get, you potentially could get a strong signal from browsing the downside maybe there's a you would have to take. This is not necessarily a downside but data from more time than a month to be getting to get enough examples to train a good model. So that's one approach. The other is that well, you know, I've been talking about this, this multi lingual model this this model that generates one vector for you know, articles across all languages and there. It's great, because the model gets learns from people all over the globe, so potentially gets, you know, a more diverse perspective on what is important the one thing that I'm currently not doing is accounting for how much data is coming from different places so it may be that readership to English Wikipedia is totally swapping out the signal from a smaller Wikipedia. But what we could do there is we could, you know, down sample from the larger, the larger languages or we could include more data from areas that are close geographically to the to the Wikipedia that we want to optimize for. So looking at vectors of people who are reading reading similar articles, who are, even if they're reading on on another wiki they are in your geographic area. Yeah, so say the geographic areas close to where other people who speak this language are. We're like trying to build something for catalan Wikipedia but the I don't know the traffic numbers but the traffic was very low. What we could do is we could reading sessions from France and Spain. Cool. Maybe that would be better than having them all be dominated by English Wikipedia readers. Yeah, a lot of interesting ways you could. You could adjust these parameters it seems like. Well, we are at time. So, thank you. Once again. Both of you. Thank you Isaac and thank you Ellery. And thank you everybody who listened in today. We will see you at our next, at our next showcase. So I have a cat showcase on March 15. Thank you, Aaron. All right. Bye bye.


Our guest today are Amherst Town Manager, Paul Bachemann, Amherst Regional Public School Superintendent, Dr. Mike Morris, and Amherst Director of Public Health, Julie Federmann. She is joining us by phone, so you can't see her at the moment, but so starting us off with some important updates is our Town Manager, Paul Bachemann. Thanks, Brianna, and thanks for helping to organize all this. So, we're living in a Zoom world, whether we like it or not, and this is where we are and for all those who are, this is the first time you've connected to Zoom, congratulations for getting through the gauntlet and making it happen. We seem to be, Julie and Mike and Brianna and I seem to be living our lives on this or Teams or some other platform like that. One of the things I first want to talk about is how well our staff is doing. These are really different circumstances and every member of our staff has really adjusted, taken on new tasks, changed their work habits, and just stepped up in a million different ways. And I just want to especially appreciate all the staff in Town Hall and Bang Center, LSSC, everywhere. Everybody is taking on doing their best they can to make sure the town continues to run, which is our commitment to you as residents of the town. This I think this crisis will actually provide some major changes to the way we do business. We're learning a lot about things that we can do differently and better and we're really excited about that going down the road, which is a long time down the road. But we're at the beginning stages of this. We think that this curve is just beginning to accelerate, it's going to go up. The governor today talked about the peak being between April 10th and April 20th. That's when the hospitals are most likely to be overloaded. That's why it's super important right now to be social, to physically distance yourself. We don't want to be socially distanced. Julie keeps thinking, correcting me on that. We want to be physically distant from each other, socially close to each other, so that we can flatten that curve so our hospitals don't get overwhelmed. It's in our own best interests, it's in our parents and children's best interests, it's in our friends and family's best interests. If you come downtown, you see a lot, the streets are kind of empty and the parking lot's plenty of parking downtown, but it's really kind of depressing a lot to be down here. But I keep thinking to myself, that's a sign of love. That's a sign of people doing what's necessary to keep the community healthy. The more we can be apart and be away from each other, the less this disease will spread. If we only spread from human to human, if we separate ourselves, it won't be able to spread. So I think that this is a real moment. It's going to be a few weeks before we hit the top and I think that it's going to be a really trying time because we feel like we've already done a couple weeks of being stuck in our homes, but it's going to be a few more weeks and these are going to be much harder. My sister was telling me that we were talking on a family thing on one of these Zoom calls and she was saying that this is what our parents went through with World War II. They all had the sacrifice and they all had to give up things because they were part of a larger effort and that's what we have to do. We have kids at home complaining because they're not able to play sports or see their friends. So I just ask you and thank everybody because it seems to be working in our state and I think in this county in particular and we can talk a little bit more about that. So really terrific work on that. First, the first thing that we always talk about, so we have a team that meets every morning and the first thing we talk about is we do a check-in to make sure our force is called force protection, make sure that our first responders are healthy and safe and have the tools that they need to do the job. We do that every morning. We check with police, fire, DPW, our town hall staff, our finance staff, IT staff, everybody to make sure. Is everybody still, how are we doing in terms of who's being quarantined, who's not? Right now, all of our first responders are in good shape. Fire department, the police department, they're doing things differently than they have in the past because they're observing the rules so if there is a domestic violence response by the police department or if there's a party that they're visiting, they'll maintain distance from the interaction, which is different than they used to do, but they're all taking precautions so that our first responders don't get infected. So and then the other thing, the other really high priority for us as our DPW employees, especially the water and wastewater treatment plant operators, they're critical to our infrastructure. The DPW has taken to staggering our shifts so that we might have a day shift and a night shift so if one shift gets infected, the other shift is still operational so they're not all working the same shift and that's been really a positive thing for us as well. You'll see DPW workers out working, you won't see as many because we're breaking them up, but there's still work they have to do. This week they have a sewer pipe that they have to clean out mostly because of flushable wipes. They're not flushable and there's a term for it, wipes don't, what is it, Brianna? Wipes clogged pipes. Wipes clogged pipes, so please don't throw down your, the gloves or the wipes down the toilet, they don't, if it doesn't disintegrate in water, it will clog the sewer line so that's a big problem for us and it becomes just a challenge to fix all these things. We're flushing our sewer mains more frequently. So thank you for all that, our town operations are doing well, there's certain things we have to do in town hall like payroll and things like that so we're trying to keep everybody as socially distanced in town hall as possible and moving forward on that. You may have heard that this weekend we closed all of the playground equipment and roped it off both for the town and for the school playgrounds. This is because we could not adequately sanitize it on a regular basis. The parks, the space themselves are open and we hope that you'll be able to use that, but again in a way where you're maintaining social distance from each other. Yesterday, the governor extended the stay at home order till May 4th and we abide by that. If there are gatherings larger than 10 people, we would like you to let us know that, and so because we will have the police respond and start to educate people. We want to limit the number of the level of gatherings that we have. Let's see, we've talked a lot about continuity of operations, it's called Coupe Plans for all of our departments. We have a new website that Breonna's been working on, it looks really sharp, and it's called AmherstCOVID19.org and on this website we're working with the school department to put as much information on one website so it's easy for you to find anything that has to do with it. So that's the way it looks and it's still a work in progress, but it's going to have tons of information up to everything that's most up to date in terms of where we are and we are open to your suggestions and ask you for comments on how we can make it better for you. Four big areas that we've spent a lot of time on, we think of them as sort of our vulnerable communities. One is our local businesses and downtown, as I mentioned earlier, it's kind of sad to be down here because there's so many businesses that have just shuttered their doors and the restaurants are doing takeout, so we're trying to support takeout businesses to keep those restaurants, keep their employees employed. It's really hard for the employees who, they're not getting their paycheck. Many people work paycheck to paycheck and this has been really hard. The Chamber of Commerce has a tip jar on their website. If you typically would tip every day, if you were buying a cup of coffee, you can go there and put some money into the tip jar. So local businesses and the people who have been employed by our local businesses are struggling and so we're meeting tomorrow actually with the bid and the chamber to talk more about some of the opportunities and options we have for them. The second is our senior population, our elders. We have a director of senior services has worked really hard to make sure that they are feeling safe and connected. We think about social distancing is also social isolating and so setting up ways for people to connect with each other have been working on that. Food security is a big thing. Do people have enough food to work? I think it's been okay so far but we're anticipating that that's going to become more of an issue. People have gotten their last paycheck but then it becomes more of an issue. To address this on Monday, the director of the Amherst Survival Center will be at the town council meeting talking about what the Survival Center is doing and then how people can help with their mission. And lastly is our homeless population.  They don't have a home so we have been working super hard every day seven days a week quite frankly to make sure that if that happens we find a place where they can rest comfortably while they they take on this this disease. It's hard to find a good place that's safe and responsive to them. So I think I'm going to stop there and I think Mike is going to talk a little bit about what the schools are up to. Good thing. So before I do I just want to really thank the town. We always from the school's perspective have a great collaboration with the town of Amherst and I think that goes really smoothly when times are good, when times are stressed or stressful or challenging. You know those relationships can go one of two ways and I just I want to you know share with the public that I feel very fortunate that our relationships only grown tighter and we have conversations nearly on a daily basis. There's plenty of days where it's multiple times per day whether it's Julie or Paul or someone else and it's it's been great and we really feel fortunate to be collaborating with the town who wants to collaborate with the schools and sees it as a partnership. So you know thank you all Paul, Julie, Brianna for your partnership and working with us and having me here on this call. Going backwards before we go forward just for folks who may not have had kids in the schools. On Friday, March 13th, I made the decision to close schools for a couple weeks and two days later the governor closed all schools in Massachusetts and since he made that initial closing he's now extended it till Monday, May 4th which as you know is the day that the stay home order ends. So many of us have some questions about the stay home order ending and kids being on buses the next morning and question you know is that going to play out and the governor is very clear in his statement that students will come back no sooner than May 4th, not that they were going to come back on May 4th. So I think we'll see how the next couple weeks goes as the town manager said you know we'll see where that how those lines look and and where we're situated but we are we're anticipating that we may go back then it may also get extended by the governor or we may feel like we need to do it ourselves and so since March 13th our schools have been closed and there's been the focus has been really on three things one's on communication, the second one on safety and the third one on distance learning and so I'll talk a little bit about communication so we've said about 15 or 16 communications since COVID since it's all started we do have a website oh thank you awesome Brianna and it has some of the basics but also you could see if Brianna scrolls down dated and we've been numbering all of our communications we have a couple number 15s because they went all went home in the same day that's what that is we've provided an FAQ document as well as additional resources linking to both the town site base state the CDC and information about our meals program which I'll speak to a little bit and we're constantly updating this site so that there's a bit of a history so people can watch the trend of what our communication has been and where it's headed all these are also shared on our social media networks Facebook Twitter Instagram and just one more I'm not even aware of because I guess I'm getting too old but you know that those are different ways and particularly for those who families who don't get our emails they don't have a child in the schools it's another way where people can find out what's happening in the schools pretty easily one of the big things that we've found is is sending videos of our principals in particular has been really helpful so especially at the elementary level our principals are sending multiple videos home to students a week and the feedback that we're getting from families is outstanding that that it really in this time and the town manager spoke about it before we're physically isolating how do we stay socially connected and the principal plays such a large role you think but an elementary school ever knows who the principal is and we've got some principals doing some you know special effects some pets are involved inside outside and but they're really making a very routine connection with students and our staff members in general just reaching out teachers para educators to families all sorts of ways you know another fun one is that tomorrow there's Fort River teachers came up with an idea to do a parade so there'll be two two different routes going through the Fort River enrollment zone of teachers staying in their cars one person per car but waving to all the students at a physically distant way just so that again that social connection can be in place and I can't wait to see it it's going to be really fun even if it's raining what kids are looking forward to seeing their teachers so much and so being able to do that in a safe way has been great the town's been supporting us with that as well so the second piece is really around safety so after schools closed in the 13th and even before that we changed our cleaning mechanisms and making sure schools are sanitized at this point our schools are locked and closed and very few people are in them so we're maintaining those but it's gotten a little bit easier the vast majority of staff members in our district are working remotely and those that aren't are working on staggered shifts so that we're reducing the number of people who are in any building at any given time we've purchased new equipment like a fogger machine that can sanitize large areas or a significant area like a school in a day and that's something that's becoming more relevant and more needed not just in the short term but we don't exactly know how things will move in the future and we want to make sure we have the best equipment to keep our schools as safe as possible and within two business days of the oh so when I think about safety I also think you know it's a matter of talked about food scarcity and that that's a safety issue from our perspective as well and so within two business days of the closure we had 13 sites and meals being delivered we know that for many families coming to a central site like a school is not necessarily a reasonable option and we also don't really want lots of cars and lots of congregation near our school sites for all the recommendations we have from CDC and the Department of Public Health so we have 13 sites in Amherst we're now partnering with UMass on the about half the sites that started the second week and to date we've delivered over 5,000 meals to our families and you know we're starting a new program tomorrow on Friday where we're going to double up the meals so that families have meals not just for Friday but you know to help with the weekend as well and we're looking to expand the program into the town of Pelham as well which is another our partner district and partner town so we feel very fortunate that our staff members have not just food service staff not just transportation staff but all staff members that were given the opportunity to volunteer and our problem is we had too many volunteers for our sites and that tells you a lot about the staff of the Amherst Regional Public Schools that they wanted to even from a distance see their students and feel like they were contributing to the food scarcity issue that's so real for many of our families and sadly only getting more real at the time manager suggested earlier. The last thing I'll speak to briefly then we'll open up for questions is distance education and this is how what supports are we providing both academically and in social emotionally for our students while they are not in the same physical location as our staff members so following the state's guidance the first two and a half weeks were focused on enrichment activities now many of our staff have maintained regular communication with our students in a whole variety of ways some of that's video conferencing audio conferencing emails using different apps and different tools at the primary grade levels there's some ones that I really need that you know books get sent home the videos of morning meeting the teachers do every morning and they send that video home to keep that structure and that normalcy for students we also had a college counselor in the first week we were done do 23 college college counseling meetings with parents and students for 11th grade students who are starting to think about college in a very real way so we are trying to continue that work we want to our kind of one of the hallmarks of the district is want to make sure each student knows who to call if they're not feeling great and that's not about this situation that's in general and we maintain that with a really strong counseling team who's very very active and reaching out to students and families during this time the state shifted the guidance for for what distance learning means last week so this week over 150 teachers and para educators got together and they formulate drafts of plans they're just went out to teachers a couple hours ago to get more feedback and what we're calling distance learning 2.0 which goes beyond enrichment activities and gets a little more into supporting students again academically and socially emotionally because of the extension of the closure and I think the teachers and para educators did a fabulous job and that'll roll out next week live once we receive more feedback from our entire faculty a major I keep saying it but I do want to stress that a major focus of these plans is not just the academic but that sense of connection that we know first you know for many many students I would argue all students that sense of connection to schools to both the peers as well as the staff members in their schools is a huge part of their life and it's very disorienting to not have that and we're trying to find the best ways we can to simulate that part of that is as the use of technology and so so far all of our middle school high school students have we have a one-to-one program so they all have Chromebooks which allows them to have that but so far we've delivered I think it's up to about 150 Chromebooks to elementary students and by the end of next week will be over 300 students that we've repurposed and made sure that elementary students had access to one of our largest problems is wi-fi access not every student in the district has wi-fi access we estimate about 95 of our students do and that still leaves 5 that do not so this week we've purchased about 95 mobile hot spots and we're in the process of getting them configured and which takes longer than I'd like but that's the nature of this this world and getting them out to families who don't have wi-fi access and we're really excited to be able to expand that program and so that it gives access not just for the academics but so many of us are relying on this technology to stay in touch socially you know certainly we're focused on the academics but but really to be able to have these kind of conversations with family with friends in different places we want to make sure that social emotional access comes along with the academic access as well so we hope to have that up and running in two weeks which will greatly close the digital divide in our district so I'll close by just saying again the partnership with community with staff with families and with the town has been critical and I think it will continue to be critical as this situation unfolds over the next couple weeks and months great thank you very much Mike for that update on our schools and all the great things you guys are doing before I open up the queue for the questions I just want to point out for those of you joining via the zoom app if you'd like to ask a question please click the Q&A button and type your question and only the host will see that question and we'll answer it in this session so one of the first questions we have is hopefully we can direct this to Julie if she can tell us how the testing for COVID-19 works can we just call in and get one or what is the process? Yes thank you for the question so the way that testing works is that only people experiencing symptoms can be tested first of all the test does not work if people aren't symptomatic that's a difficult thing I think for people to understand but it takes the presence of symptoms which indicates that there's enough of a viral load in the body that a test will actually be able to detect the disease so the next piece of this is that there are still not enough test kits the people who will get tested are those who are experiencing fairly moderate to severe symptoms so a person's first step is to context their primary care provider by telephone to talk with them and tell them how they're feeling their primary care provider then triages over the phone with them to figure out just exactly what their symptoms are and if they really do meet the criteria for how we're diagnosing COVID-19 many people will just be told to stay home to recover and to stay in touch with their primary care provider if they start to have worsening symptoms because many many people will not require hospitalization and will be able to recover at home tests are needing to be saved for those who are severely ill and so some people will be asked to drive to their PCP's office be given a mask and met outside where they'll be tested this won't happen for the majority of people at this time once someone is tested that result comes back in one to two days and then whether a person comes back tested positive or clinically diagnosed over the phone or by teleconference as having COVID-19 they'll be asked to isolate at home if a person is tested the result of those tests are sent directly from all labs to the state health department the state health department then notifies the time where the individual lives so in this case if you're an Amherst resident it it notifies the Amherst health department confidentially via an electronic system we then call someone up who has had a positive case go over with them how they're feeling their understanding of isolation and what that means staying in the home not sharing a bedroom or bedclothes or towels or dishes and if if they're able to have their own bathroom if they're not able to have their own bathroom then advising them to wipe down the bathroom with Clorox or Lysol after they use it and then returning to their own room so not having contact with anyone until 72 hours after they have no fever and no other signs of cough or shortness of breath and they have not taken any fever reducing medications for 72 hours at that point they would call their doctor review all this with them and be released from isolation meanwhile at that first call the nurses in our office will be going over with the person who's tested positive any possible contact they've had whether in the household or outside in the community we then immediately contact all of those people this is what's known as contact tracing we call them and review with them that they have come in contact with someone who tested positive and that they need to go into quarantine quarantine physically is the same as isolation so you're not sharing a bedroom you're not sharing towels or dishes preferably not sharing a bathroom but if you are cleaning that bathroom the difference is that if you're quarantined you're not sick you're not experiencing symptoms you are staying in quarantine for 14 days because that's the incubation period for the disease and if during that time you start to develop some symptoms of COVID-19 a fever over 100 degrees cough shortness of breath then you are to call your primary care provider so they can then triage with you to see if you have developed the disease if at the end of 14 days a person is feeling completely well they leave quarantine they are not sick they are not contagious in any way and quarantine ends great thank you Julie so next question is are there any confirmed cases in Amherst and if so how many yes we have had some cases in Amherst at this time we are reporting the number of cases by county so in Hampshire County today we have 186 cases the reason that we're not talking about the number of cases in our particular town is because we don't have many and the advice from the Department of Public Health is that when we just have small numbers in a community it's really important to not release those numbers because it could lead to compromising the privacy of the people who are experiencing the illness and with communicable diseases people do have the privacy to not have others being able to know that they're sick and that's why at this time we're not announcing a particular number the number is low thank you so we have a question here about town operations they want to know what is happening with dog licenses at the moment Paul you're on mute Paul so thank you um but first thank you Julie it's just a side note that it's really terrific that our town has Julie Thetterman and Jen Brown who are registered nurses working in our health department at during this critical time they both have decades of experience and this area in particular and it's just been fabulous because so many of our decisions are based on science and we need uh science scientists like nurses to help us interpret what the information that we're reading what we're receiving is all about so about dog licenses so this this uh pandemic affects everybody in different ways so we are processing dog licenses we have 210 dog licenses that have been submitted so far this year they are being processed through the town clerk's office we have a skeleton crew and the town clerk's office they're rotating through on shifts and we should have them all all the ones that are in as of today the 210 they should be out in the mail to you on by wednesday all right uh we have a follow-up here for Julie what should be done if you do not have a primary care physician who should you who should you contact thank you so i'm going to give you the number for um Cooley Cooley Dickinson helpline they are linking people with healthcare providers if you don't have one it's a good idea to do that now um so that you can get all set up in case you are sick they're also helping people to be linked up with health insurance so that number is seven days a week from eight thirty to four thirty the number is eight eight eight five five four four two three four so we do urge people to a primary care provider work on getting health insurance no one should be going to an urgent care or an emergency room or a doctor's office if they're feeling sick they should make a call first now if you are experiencing severe symptoms of shortness of breath and a high fever then you would call 911 and describe the symptoms that you're having if you don't have a primary care provider and then you could be transferred to the hospital for assessment but we're trying to avoid that because we don't want people leaving their homes and exposing others to the disease we also want everyone to get care so again we urge you to get a health care provider now if you can we will add this resource to our amherstcovid19.org website as well one of the questions just came in if we would do that and we will okay so we have a question here for the for mic morris our questioner wants to know can elementary school students have more structured zoom type teaching teachings given the school closure is so long yeah so that's really uh what I referenced earlier is the teachers are developing plans for distance learning 2.0 which will have much more opportunities for direct teaching uh the guidance we received from the state is that we shouldn't be exploring new content but taking existing content and going deeper into it so you know as we move forward there has been some examples of that and there's a couple different ways to do it one is live like what we're doing right now another thing that maybe worked really well for families is to have tape lessons where teachers teach a lesson and send out the video because we know for many families it may be hard it's not like the kids are getting dropped off of the bus and everyone's in school at nine o'clock for the lesson so I think what people will see is a mix of those two things moving forward and again that will start up on April 9th and then really an artist the week of April 13th will there be a really an increase from again from enrichment activities which was the guidance when we first closed to having much more instruction and much more direct connection in those ways so I think I would say that we have a really I think both locally and statewide there's an incredibly diverse schools incredibly diverse schools of thought about it there are people who don't want their children especially elementary school children on screens for long periods of time and there's other folks who are very comfortable with that and the way we're approaching is we're going to offer those that instruction and those skills to families and families can make the choice they think that's in the best interest of their child but I do want to acknowledge that there are some real differences of opinion of families and we're trying to strike it down the middle where we're offering the service for students and families if they so choose to take it there also will be other you know non-electronic resources that go home but I think for families they would I would expect to see an increase from as we move from enrichment to the implementation of distance learning 2.0 great thank you we have a another follow-up question on contact tracing and this person wants to know how do you how do we conduct contact tracing for the confirmed cases in Amherst thank you for the question so in Massachusetts there are over 60 plus reportable communicable diseases so for a couple of decades we've been the two of us who are working now have been tracking contacts for all of these diseases in various settings COVID-19 is just another more complex disease because it is so contagious that there are so many contacts so what we do is we have a very deep interview with the case the person who is ill we talk with them about who they live with what types of things they do where they work where they recreate and then we go deeper talking with them about what they had been doing during the period of time that they were contagious we then get names and phone numbers and addresses if we're not addresses names and phone numbers if we're able to if we're not able to get all that information at least we get names and then we track those down we then have confidential phone phone calls with all of those contacts so anyone who has come in contact with the case receives a phone call from us if it's someone who doesn't have a phone because that does happen sometimes then we send letters we do door knocks we do everything that we can to find a contact to let them know that they've come in contact with this transmissible disease and because we have so much experience doing this with so many different types of communicable diseases we have become quite adept at finding people and helping them to understand how to stay has to protect themselves from getting sick and from spreading the disease great thank you we have um we have a question about is there any advice if it's safe to ride in lifts and ubers right now that's a great question lift and uber all over the country have been very assertive in training their staff on how they should handle the interiors of their vehicle so lift and uber have been considered to be safe one percussion that people can take is having chlorox or lysol wipes with them wiping the door handle on the inside and the outside when they enter a vehicle and again uber drivers and lift drivers have been reached out to because they also want to be able to stay safe and healthy at the same time so they are cleaning their vehicle and being taught about how this disease is transmitted great thank you um we've got a question here or a statement rather um things are very stressful in the house right now with everyone home is there any advice that you can give us that's open to anybody to answer i can jump in on start on that one if that's okay uh identify with that um you know i've got young kids and and uh you know i can identify that and that's true no matter if kids are not but i think that's something i hear given my role quite a bit and so uh one thing i'll say and i think everyone on the call will agree with us and in physically distanced ways spend time outside every single day even if it's raining spend time every single day um i think another thing that's really helped that i've read a lot about is uh whatever the routine you want to have try to maintain some routine in this in this new world where people are in their homes a lot whether that's having a morning meeting your family whatever your rituals you want to have having adding some structure that that pushes you all to stop and and and be with each other and not try to scramble between work and how to get everything done and cooking and you know for a lot of people who used to order out like this is a big challenge right where you can get your food and how to plan that out but really trying to preserve time i think another thing and i said this in the earlier call is um make sure you're reaching out to people uh in your networks oftentimes people you don't always reach out to people are reaching out to you to check in make sure you're returning that favor it's amazing how uh to in my personal life i have two uh meetings like this except not public not recorded but with groups of people that um i certainly could have connected with any number of times once a group of college friends once for family and the number of people who want to jump in even for a 20 minute 30 minute call given the situation is enormous so trying to be the person who checks in on people who creates social capital you know within your group of friends that you are connecting or your family is hugely important and also being kind to yourself i think that's something that i'm noticing as everyone's trying to juggle more hats and they're used to juggling in a more confined space it may mean that you're not cleaning up as much as you'd like in your house and that can be really stressful for people i mean those small things really matter so just making sure that you're you're taking care of yourself especially if you're a caretaker for whether it's children or parents or other family members that you're finding ways to give yourself a break and give yourself time every day to acknowledge that one one thing that we're doing in a lot of my administrative meetings with staff with my kind of leadership team it's just starting the day every meeting we try to dedicate time to start sharing something that the good that's happening whether it's work related or not work related but just sometimes acknowledging the good things that are happening in the midst of this stress and hearing other people's can be a really helpful thing as well sorry i was long winded paul and julian briana you something can jump in but it's something that that we're deeply concerned about because we know how stressful this is for children and families and we experience it ourselves as well i'll just add a little piece to that i think with our team it's really about being forgiving of each other because we're making decisions every day people are doing the best that they can we might make a bad decision we might adjust it the next day but just giving people leeway to say we recognize you're really really strong people in our jobs but they're making the best decision they cannot each day and then you know just being forgiving like we get it let's move on to the next issue i think that's great i'll just reiterate what mike was saying that i think we are we can look at it that we're so lucky that springtime is coming and this is when this is happening we're not headed into winter i've recognized there are some people that love winter but really i think all of us can relate to the blooming of the spring and how beautiful that is and i think that helping our families and ourselves focus on what we can be grateful for now during these really tough times i think it really helps to decrease our stress when we're thinking about others too great thank you i think that is all we have for questions for now all the time we have so i want to thank paul mike and julie for their time today and to all of you for joining us we will have more information on future events like this so stay tuned again if you need to follow up with us or we were unable to answer your question directly today please email us at info at amherstma.gov or you can call the town manager's office at<PHONE_NUMBER> thank you thanks everybody stay safe and healthy thank you


The first ever conference matchup the colonial hockey conference comes to Reisterstown as the Mustangs welcome in the Becker Hawks One by Taggart Antonelli tries to clear Caponelli comes and cleans it up and dumps it in Caponelli's gonna chase after it again. Remember her first goal of the season last year was a short-hander up at the Plymouth State Tournament here to turn over in front Taggart with a chance back in rebound Taggart scores The Becker part that was Caponelli Taggart with another assist that before Taggart is her fourth assist of the season and a score again Another short-handed goal. I turned around to look at the stat sheet. The Mustangs are on the board once again Partial line change here the head it goes to Alexander Alexander at center nifty move She's got one-on-one here that has Caponelli about the bigger part that is out Pose it rather behind her pose it get into the far corner to heads it to Abbott Abbott with a shot score Abbott on the board. It's three-nothing Mustangs Mustangs with it Weber at the blue line trying to keep it in they're able to do so Alice back hands one to reach you on the near side. Richie will shoot one in a nice one-timer for Lang. She scores Goes and all to Weber Weber ahead to Madison Lang Lang trying to catch the Hawks midline change She's gonna go one-on-one here Madison Lang and on goal a chance score Terrific individual effort there from Madison Lang makes it five to nothing Mustangs Pose it along the boards to Blackburn Blackburn now in the far corner Blackburn corrals it gets it to Abbott Abbott back to Blackburn Blackburn nifty chance Johnson with the same rebound score Now some help arrives as the puck comes up to Antonelli on the near sideboards now in the near corner Antonelli standing guard as Lang that gets the puck in the near corner Lang turns around shoots one in front now a chance for Alice score She'll see Ellis gets a goal with seven to nothing off the boards. It goes And back to Antonelli It's an early will risk one in Mustangs trying for the deflection score Let's see who got that one It might be Blackburn and if it is it's a hat trick for Chelsea