Ladies and gentlemen, please welcome Santosh, who is going to be talking about machine learning on our projects. Hello, hello, hello. Hello and welcome everyone and hello to everyone watching this online too. So I hope everybody is doing good. Today morning I was attending two sessions, both on Generative AI. People were very interesting discussions, whether Generative AI can create articles. How will it scale to the scale of languages that we are supporting. But this session is about the current state of machine learning techniques used in Wikipedia projects. It's not about the future, it's about the current states. I am Sandosh Tautengal. I am a Principal Software Engineer at Wikimedia Foundation. So in the next 30 minutes, I will be talking about the existing major projects and briefly. And I will go through its use cases, guiding principles, product design, challenges and their impact in the old Wikimedia universe. The guiding principles of using machine learning technique in Wikipedia projects is not complex, it's simple. It's all about empowering our users, whether they are editors or whether they are just users. And we want to make sure that we are not discriminating based on any language. We want to make sure that all the tools that we are developing should be available in as much as many languages as possible. So that's one of the guiding principles. Second thing is we want to make sure that all the editors and users sender in the workflow. They are in charge of creating content. They are the masters. The AI is just for assisting them to save their effort, to save their time. But instead of going through these abstract principles, I will use the existing projects in Wikimedia Foundation and I will explain how the product design, the use case and how people are using exemplify these principles. I will start with one of the major projects within the Wikimedia Foundation called content translation. It uses machine translation technique and this was started back in 2015. Content translation is about translating articles from one language to another language when it doesn't exist in the target language. So this requires a slightly different skill for the users. They should be bilingual. They should know multiple languages for obvious reasons. And they don't necessarily need to know the complexities of wiki editing like wiki markup or wiki text or anything or references because the article already exists in another language. So they need to translate according to the grammar rules and it should read very naturally in the target language. So that's the skill it requires. We provide this tool to address one major problem in the Wikimedia Universe that is language gap, which is a well-known problem. Everybody is aware of this issue. We have 6 million plus articles in English Wikipedia. We have 2 million plus articles in German Wikipedia and then we have 657k plus articles in Indonesia, Telugu, 84k plus and then we have a long series of all these languages with different sizes and we have 320 languages. So if you look at this illustration, please don't confuse that the knowledge gap issue does not exist for English. English is also facing the same issue. So for example, there are articles exist in German Wikipedia or Indonesian Wikipedia or Telugu Wikipedia that does not exist in English. So English can also benefit from transferring knowledge from other smaller Wikipedias to English. So it's not one direction from a large single Wikipedia to smaller Wikipedia. So this is our language gap issue and this is one of the core issue that we want to try to solve using the content translation tool. But I want to emphasize that this is not the single solution to address issue. It's a complicated issue about the knowledge gap. This is just one solution to address issue. The way we design the product is to make sure that it's easy for everybody to use. We have this source article in the left side that's maybe a source language for example English and then you have a placeholder where you can add more and more content in your target language and then you will be presented with a lot of tools to work with this one. So as a user, they need to click on these placeholders like the add tab translation and they will be presented with an initial version of the translation of the source article as the placeholder and we will use machine translation wherever available to fill this content. So you have a draft version of the translation already presented using machine translation. Then you need to edit it. So if you think that you can just use the machine translation and publish your mistake and that's not the concept here. You are presented with an initial version and you need to edit it. You need to make sure that it is reading naturally according to your language because we know that machine translation is not always perfect. Its quality varies per language. Sometimes it's good, sometimes it's not bad, sometimes it can get facts wrong, sometimes it can get grammar wrong. You need to do all these edits but then only you can publish it and we also don't need to use the literal translation of everything. You can edit it, you can remove certain parts, you can make it culturally appropriate and then you can try to publish. But if there is some people who just want to publish without human edits, we do measure it, like how many post-edits are doing on top of machine translation and we will show you the amount of edits, amount of curation that you did in a percentage scale and we have a threshold. This threshold is like how much percentage of the human edits need to happen before a translated article published in a target Wikipedia. This threshold is not decided by us but decided by the community. The community tells like, ok, 95% of the machine translation is allowed in this wiki. Depending on the current quality of the machine translation, you send the current machine translation engine. So we enforce that threshold and if this is not followed, we will prevent people from publishing it. So this is how the machine translation quality is assured in every Wikipedia. So just to go back to the guiding principles, so here when you publish an article, it's not the machine translation engine publishing the article. It's the users publishing this article. It's up to the users to make sure that the article meets the quality of the target Wikipedia. The article is attributed to this editor. They are using machine translation only to save their time, to save their effort and to make best use of their time. So that is the principle. So far with this tool, the impact since it was started in 2015, we have published 1.6 million articles using this tool. Just to get a sense of that, if you combine all these 1.6 million articles into a single Wikipedia, this would be one of the top 10 Wikipedias we have. So we were also measuring whether the published articles are meeting the standards of that wiki. So we were measuring whether these articles are getting deleted based on the community standard later. And we were seeing that 4% of these published articles were getting deleted later for various reasons. But if you want to understand how this 4% is good or bad, we have an average 13% of deletion rate if you start an article from scratch in that Wikipedia using any editor. So if you start without translation, there is a chance, a probability, that your article will get deleted 13%. It's a rough estimate. But here it is, if it is published using machine translation, it is 4%. There is no machine translation engine that can support all the 320 languages, right? To integrate it with the current translation tool. So for that, we have been integrating various machine translation engines into the system since the beginning of this tool. And then only we can cover all the languages we can cover. So we started with Aperchium in 2015. At that point of time, that was one of the existing free and open source system with good quality for many of the languages. I want to note that Aperchium is not a machine learning based machine translation system. It is a rule-based machine translation system. It's free and open source. We used Aperchium for 45 languages. And then based on the community request, we were asked to integrate Google as well, Google machine translation service. So we collaborated with Google. We got discounted API credits to use integrated Google. We also got community feedback that they wanted to use Yandex machine translation engine for Russian and related languages. So we collaborated with Yandex. And we integrated Yandex also with the machine translation tool. Then we got feedback from the Chinese community that the Lingokloud machine translation engine is good for English Chinese and Chinese to English machine translation. So we integrated that as well. We also integrated Ilya for bus and related languages. So combined with all these languages, for example, Google is covering 134 languages. Yandex is covering 99 languages. Lingokloud, Ilya are supporting some other languages. But I want to mention that some of the languages will be covered by multiple engines. And we provide this as a choice to the users. So they can choose the best engine that means their needs and that is best for their language. So that's why we have multiple engines. And we give this as a choice to the users to pick the correct one. But even with the combined all of these things, we had multiple problems. One is dependency on these external machine transition engines is not sustainable. That's one thing. Second thing is like if you want to scale up the machine translation capability in our infrastructure, to more and more use cases, we should have our own capability on the machine translation. So for that, we were working this year to launch our own machine translation engine, a machine translation engine hosted within the Wikimedia Foundation that we can use it for any purpose in the Wikimedia Foundation. And we named it as MIND, M-I-N-T, the last one, it's self-hosted. We announced this in last June. This is a free and open source machine translation engine we hosted in our infrastructure. It provides a single unified machine translation API so that it can be integrated in various tools. And it is powered by various neural machine transition engines starting from NLLB. NLLB is a project by Meta, Facebook. NLLB stands for No Language Left Behind. So it supports so many languages. We collaborated, Wikimedia Foundation collaborated with Meta. And we also have Wikipedia fine-tuned machine translation models from NLLB called NLLB Wikipedia to produce content that are according to the Wikipedia style of writing. And then we have OpusMT, by OpusMT project by University of Helsinki to support some of the very low-resource languages that are not supported by any other commercial or open-source empty engines. We also got some feedback from the Catalan Wikipedia that the soft Catalan neural machine translation engine is good for, I mean, it's the state of the art for the English Catalan languages so integrated. All of these are like open-source license. I need to mention that. And then recently, the IndicTrans2 project by IIT Chennai, they released machine translation model for 22 Indian languages and from feedback and by testing we found that that's the state of the art machine translation model for 22 Indian languages from English or from these languages to English. So we integrated that as well. So combining all these backends, we provide a unified interface and we got the capability to support machine translation for 198 languages, which would be, I think, probably one of the largest machine translation system in production anywhere in the world, I guess. And when I say 198 languages, it's the unique number of languages. We allow translation between these languages to any other language, right? So currently, we are having, if we count that one, we are supporting 35,924 language pairs for translation between any of these languages. So you can translate between any of these languages, right? So another advantage with having this self-hosted machine translation system is like we can integrate with more and more products in Wikimedia Foundation to reduce the knowledge gap, to make users accessible and reduce the barrier of language. We recently integrated with our localization platform called translatewiki.net, where we translate, localize our software interface. This machine translation engine is also currently used in that system. Let's talk about knowledge integrity. Here we are talking about vandalism prevention and patrolling, helping people to review the edits happening in Wikipedia. So here, this project is one of the oldest machine learning projects within the Wikimedia Foundation. This was started back in 2015, and it had a lot of press coverage of how we use machine learning for making Wikipedia and its knowledge integrity to vandalism prevention and making the reviewers' job very easy. We also have this research published in various places about how to make this machine learning a participatory machine learning, where we collaborate with the communities to make this machine learning work for their language. In basic terms, this works in this way. So you have a lot of edits happening in every Wikipedia. All these edits will be classified into certain categories. This is a good edit, this is a bad edit, or this needs review into certain categories. And this was named as objective revision evaluation service called ORS in those days. So this is the basic technique how it is working. Recently, sorry, this evaluation will be further presented to the reviewers in special recent changes pages. Whenever you go to recent changes pages, it will be with some highlighting so that reviewers can filter it, and they can choose which one is good, which one is bad, and they can do their own decisions. I need to note that this is just to inform the reviewers and the machine translation or, sorry, machine learning tool does not make any decisions about your edit. It does not revert the content. It does not make any decisions on this one. It just informs the reviewers that the quality of the content. So here the editors are in the loop. They are in the charts, reviewers are in the charts, and they can also set thresholds, how strict it should be, how less strict it should be in certain wikis according to their preferences. But this system is getting modernized these days. It's getting the actual system that started many years back is getting deprecated now, ORS is getting deprecated. A new model that is coming these days, it's called the revert risk model, and it is hosted in our modern machine learning hosting platform called Lift Wing. This is already in production. One reason why it is getting modernized is to support more and more languages. It required a lot of work to support more languages to do this revision scoring. Currently, there are two experiments happening on this one. One is to use a large language model called EMPR to score these edits. And the another one is like, I think this is the direction we want to move. It's the language agnostic scoring system where we can score these edits independent of the languages. So this table shows just an comparison. Irrespective of whether which model we are using, the whole machine learning model, both of these are trained on the past reverts. The past history of all the edits happening in every Wikipedia. So that's where this was trained. If we are using a large language model, you might already know that it requires a lot of computing power. Training them is expensive and they are not good about supporting all the languages. So I think EMPR supports 47 languages. But we want to make sure that this scales beyond 47 available for all the languages. So we are working on the language agnostic scoring system and we are rolling it out slowly. It's in the initial days. Let's talk about another use case of machine learning here. Structured tasks. So here we are talking about very simple tasks that we can suggest to the newcomers, new people who registered on Wikis. So they get an idea like what to contribute. I know that I can contribute in Wikipedia, but what to contribute? This tool suggests a simple task, adding links to an existing article where it is missing. So this takes a new newly registered user in a simple workflow. So first this will be presented in their home page, newcomer home page. And they will be informed about this kind of suggestions. And in the second stage, in the onboarding workflow, it will tell you the importance of adding links, how it helps the readers. In the third step, it will warn you or inform you that this is a suggestion by a machine learning system. And you need to make your informed decision about whether adding a link is OK or not, whether the suggestion is good or not.  Then you are presented with an article. You are given with a suggestion. And you need to see what are the details of the link, where the link goes, and whether it is the correct link, whether it makes sense to add there. And then you can say yes or no. And if you press yes, that link will be added. And you can see things are published in the article. Later, you are also encouraged to continue this kind of contribution in the wiki. So this is the simple workflow where we are taking the newcomers into this one. How this is working? This is working based on training, existing sentences, the existing links, the missing links. And then we generate a list of candidates that we can suggest and we score it. We present to the users. That's the algorithm in very simple terms. The impact we are seeing with this kind of suggestions and newcomers is very positive. This is a rough estimate of our impact so far. We are seeing activation, 17% of increased activation. It's the probability that the newcomer makes their first edit, rather than just registering and not doing any edits at all. Secondly, we are seeing 16% progress in the retention that the newcomer is retained. They continue to edit in that wikipedia. And then productivity, that they increase the number of edits newcomers make during their first couple of weeks. These are all rough estimates. It's slightly difficult to calculate how it is working. And then we are also seeing the revert rate, decreasing the reverts rate compared to a newcomer, compared to the edits that a typical newcomer will do without this kind of suggestions. So we are seeing a decrease in that revert risk, reverts, that is minus 11%. But these are all rough estimates about how this tool is working and its impact. There are also few challenges with this kind of system, like if newcomers are adding links, moderates may say that it's more work for them to review. And if you want to support more and more languages, we need to have more models. And to train that models, we need more data. If the wiki has less amount of data, training may not be efficient as compared to a large language. Let's talk about a symbol, another machine learning technique we are using, optical character recognition. This is used in wiki source for digitizing historic documents or any other documents so that we can host it in the wiki source. Here we are using Tesseract, one of the free and open source machine learning system for optical character recognition to scan the document, convert that into a text. That's the optical character recognition I'm talking about. This is used in 100 plus... Tesseract is available in 100 plus languages. Plus, we are using transcribers. It's an externally hosted system. We are using it for historic documents, handwritten in palm leaves, and other sources which are not printed. So we use it transcribers as well. We are also using Google Cloud Vision OCR for the same purpose. I want to talk about the lift wing, which is our effort by the machine learning team of the wiki media foundation to modernize our platform so that we can easily host machine learning services in our infrastructure in a simple micro-services where people can host easily new machine learning models. We can deploy, we can iterate on that one, and it's fault isolation, all those requirements that we need for a modern machine learning platform. It roughly works like this. People need to prepare that model as a case-based system running on a Kubernetes, but those are technical details. If you don't know, just skip it. But this will be clearly documented using model cards. I will talk about model cards in a few minutes, but that's about transparency about these models. Then model cards can be used for the transfer into the community, and all these models will be exposed to a single APA gateway so that external tools or all the projects can integrate into this machine learning platform. There are many other machine learning use cases we are using. I'm going to list a few. I want to make sure that this is not the full list of machine learning projects we have. There are machine learning projects by foundation. There are also so many machine learning tools by our volunteers, our boards, and several services. So I just need to acknowledge that this is not the full list of all of them. There are some examples I just chose for this presentation. So we use one machine learning techniques to topic classification. Topic classification is to study the knowledge gap. So to find out which topic an article belongs to, given an article, it will tell you which topic it goes to. So by measuring this, we can say that some topic is lacking a lot of articles, or some topic has more articles than some other topics. So that's one thing. Second thing is like language identification system. Language identification is given a snippet of text. It will tell you which language it is written. So it supports 200 plus languages. Then we are also having a mobile version of the content transition tool where you can translate articles. It works by translating one sentence at a time and it's translating sections between articles, no matter whether it is existing or not. But we want to make sure that whether a certain section is present in one language but not in another language. The section title might be different. The content might be different. So we want to know whether they are talking about the same thing before we suggest to translate that section. So we use machine learning for that as well. So there are also a lot of machine learning tools that are not in Wikimedia Foundation infrastructure. We collaborate with several third party vendors for this one. I already mentioned about Google, Yandex, and area for machine translation. For text-to-speech, we are trying to see whether we can have our own text-to-speech engine that can cover more and more languages. Machine vision is used for debit statements like an image is given. You need to tell what it is in the image. Google OCR API and transcribers for optical character condition. London moderation can use to detect plagiarism and et cetera for using Ternitin, named entity recognition to find out what are the Wikidata entities mentioned in a given paraphrase from the Wikipedia. So these are some of the third party machine learning services that we use currently. Not a full list but some examples. Finally, I want to talk about model cards. This is all about transparency, about how we use machine learning inside Wikimedia. So for every model that we are using in the Wikimedia Foundation, we want to be transparent to the community. We want to document the use cases. Who are the users of this model, potential use cases? Which data is used for training this model? It's ethical consideration, if any. We need to be transparent about ethical consideration. Who is developing this model? Who is the current owner? Who is the contact person for this model? The license and model architecture so that people can understand how this is working. We briefly mentioned in the last session also. This is a recommendation from the leaders in the machine learning industry about being transparent about how we are using machine learning models in production and how we help people. So this is our effort for documenting all the machine learning tools we are using. Here is an example. So you will see a model card like this in Wikimedia. So this is an example. If you go there, you will be seeing a lot of details about every machine learning tool we are using. Okay, here is an example. All right, I think that's all I have today. I hope it was informative. We have only like two minutes if there are questions. I will see if there are online questions or from here. Hello. Hi. I'm Diego from Spain. I take a lot of pictures. I have a question about the translation tool I have been using for a while. And if you don't make many changes in the text it will not be accepted. You had a screenshot about this. And I was wondering when will you decrease the amount of text that needs to be changed to get this translation accepted because as the translation machines are getting better and better the need to change something in the text is getting lower and lower. So right now what I'm doing is making artificial changes in the text so that it gets accepted and later on I have to take it out. Right, got it. Yeah, that's a good question. As the quality of the machine translation goes you might need not do any change at all in the machine translation provided by the system. And we have been getting this feedback that we need to adjust our thresholds to allow this kind of use cases. And we were also already doing this one based on the community request they asked us to relax it, relax it and we are accepting it this one. So it's very simple to do this one. We will increase the limit so that you will be able to publish without much changes that it used to be. Because that also shows the advancements of the machine translation quality happening in the newer and newer version of the machine translation tools. We can do that, just file a ticket, we'll do that. Okay, thank you. Second question is you mentioned that it's possible to choose the machine for the translation, how can you do that? In the screenshot I was showing the right side tools there will be a dropdown where you can choose which machine translation engine you need to use Google, Mint or you can even choose not to use any machine translation engine and write the translation from scratch. It's there in the tools in the third column of the session. Okay, and is there somewhere an overview of what tools can translate which kind of text better than others? I didn't get that question. You know the machines but the normal user doesn't know which machine is better for what kind of translation. That's a good question. So which model to choose, which machine translation system to use? We ask community to tell which one should be the default. Then community tell us by doing a lot of evaluation and then we just make that as the default. If there are multiple choices, the default choice will be always the free and open source one. That's our based on policy. But if community is saying that one system is better than other, we will make it default. But once you choose, you can also make sure that that will be useful for all your future translations. So that's also possible. Okay, thank you. Thank you. I think we are at the most end of the time. Thank you for attending. Thank you for only people watching this one. Have a good day. Thank you, Santosh. Everyone, we've got some lovely bento boxes for lunch at the back here and come back to join us at 1.45 for the next session.


Hi, my name's Zach and I'm from Pasco, Washington. And my life before joining the worship team was complete emptiness. I just lived my life day by day going to school, going to my friend's house, playing games. And I came to church because I'm a pastor's kid, so you kind of have to. But once I joined the worship team, I realized that my life cannot be the same as it was before joining the worship team. So then I gave my life to Christ, which was amazing. And I realized that I have a purpose for my life and it's to win souls. So my goals for 2015 is to start a home group and grow in a deeper relationship with the Holy Spirit and try to bring as many people to Christ as I can. So today I'm getting baptized to show people that I made the decision of following Christ and to make him my Lord and Savior. Zach, do you believe in Jesus Christ as God's Son and your personal Lord and Savior? Yes. Do you promise to follow Him with good and clear conscience all the days of your life? Yes. Based on your faith, confession, and your commitment, we will baptize you in the name of the Father, Son, and the Holy Ghost. Chains are broken, cause you spoke it is finished at the cross.


My name is Amanda Whaley. I work with Cisco DevNet. I lead our Developer Evangelism and Experience Group. Cisco DevNet is the developer community for Cisco. We're reaching across app developers, infrastructure developers, network operators. It's a very inclusive and wide audience of developers working on IOT, data center, location-aware apps, collaboration across all these areas. One of our core beliefs is that hands-on learning and getting hands-on with all the platforms and the materials you need to learn and grow as a developer and learn about new technologies is one of our core strengths. It's an area we invest a lot of effort and thought into. DevNet Create is the new event that we're launching this spring, and we are creating this event to bring together this wide group of developers. We're going to bring our interest in hands-on learning, hands-on hacks, many hacks, exercises you can do at the event and take home back to your team so that the experience of the event can grow beyond just the people who are at the event. I am really excited about one, meeting everybody who's going to be there. Two, I'm excited about building this conversation, this conversation around where apps meet people, where apps meet places, where apps meet things, all the places where apps even meet other apps. And we're getting everyone together to start that conversation and really define the next stages in that growth. You can go to devnetcreate.io, that's the site for the conference. You can submit a talk, you can register to attend. You'll be able to find video and materials that are going to be associated with the conference there. It is May 23rd and 24th in San Francisco. The call for papers is open now. You're welcome to submit.


Welcome folks, we have the Dow Industries trading up $65, Nasdaq's down $34, S&P's up $7.5, Gold Gold Contract down $2 traded at $18.19 an ounce, we have Silver down $12 cents, $20.75 cents an ounce, Platinum trading up $5 at $9.10 an ounce, Light Sweet Crude up $220.109.56, Loots and Bonds, a 10-year note, up 25 ticks, trading $1.1716, 30-year up a full point, plus 20 ticks, two ticks at $136.27 and $Kingdollar, $Kingdollars up $611 ticks, trading at $105.11, the Euro is out here at a price point of $104, the yen is at $136.55, and the British pound is at $1.21 to $1.00 USD. We get open, we take a look at this marketplace, what it's good to have out here is this, you get a sideways move, yesterday was a really cool day. And what I mean by that is this, is that you had the S&P and the Dow go to a higher price with lighter volume and give it up. The NASDAQ went lower with heavier volume. The correlation is very hard to do, and it's another confirmation that we're going lower in a big way, specifically what, and I'll explain it when we start this program out. Spy right now is down 87, you trade 87 cents, this is, trading at 47 million shares, bottom line, we take a look at the NDX100, inside the 3Qs, 3Qs out here trading down 42 cents, 46 million shares, so you get, basically there's a sideways move. Now, gold, gold had caught bid this morning folks and gave it up, it's not unbelievable because when we go to the dollar, you're going to see what happened. So gold had some action going this morning, it did push higher, had 144,000 contracts, that being said, you still got a sideways move. Platinum is held, platinum was up $12 earlier, you're up $5 now, we got to $9.33, you're at $9.10, and let's just go to the dollar because the dollar, the more I keep looking at this, bringing it up yesterday, this dollar, bottom line looks to me like number one, let's see, $105, you're already over it, you're already over the low, this dollar wants to go to $1.21, man. This is going to be pretty intense. We go to $1.21, and that's going to be one of the core reasons that you're going to see this whole ABC structure get filled, and more than likely what we're going to see is that we're going to be down at the highs of the lows on all the indices of March 2020. Stay right there folks, come right back. Dow, and dust yields up 36, Nasdaq's down 38, S&P's off 10, we'll come right back.


So what I would like to cover is some of the things we have been doing at La Trobe for a couple of years in training up research, in librarians in research data management and sort of where we have come from and where we are going to. So I would just like to acknowledge that this presentation is sort of an adaptation of one we did at the e-research conference last year in Melbourne and that was done with Kerry Sullivan and Aminah Nichols-Boyd as well but this is sort of an update of where we've come from and where we're going to. Just I guess about La Trobe we we have this wonderful statue in our Bandura campus, our Melbourne campus by the sculptor Charles Robb which really sort of gives an indication of what we're going to sort of cover today and I guess what I'll do is I'll also talk a little bit about La Trobe if people don't know that we were established in 1964 and we opened in 1967. We have about 30,000 students and we're across five different campuses across the state of Victoria and today I'm at our wonderful Aubrey-Wedonga campus and we have other regional campuses in Victoria and our main campus in Melbourne where the bulk of our students are. I guess the metaphor here is that we this sculpture is about universities turning ideas on their heads and I guess our journey with our librarians learning about research data management for them is really turning them on their heads and thinking differently about the work that they do and how they support researchers and so yeah I guess what we were coming from was a situation where we had librarians who were supporting teaching and learning and research and doing their usual what we called faculty librarian work and then coming in and into the more technical area of the library which was the digital infrastructure area last year and the year before in terms of looking at the repository and research data management and more technical aspects of that work and discovering what was being done there and how they could then advocate for that and talk to researchers and support their needs and so I guess the other metaphor for the statue on its head is and that the statues of Governor Charles La Trobe actually I should say also in case you didn't notice the other metaphor is that the university has really turned itself on its head after a big restructure last year so that's you know been a really big change for the university and the library is positioning itself to to help with that so what we've been what the university has been talking about is it's a future ready agenda so we've been looking at it's the strategic plan and the research plan which is has a really strong emphasis on supporting researchers and really making sure that they're productive and that their research is well known and well described that they can that we we really help them promote that research and that we we position ourselves to really really help them in terms of their what their outputs are and in order for people to be productive to also you know in I guess improve La Trobe's situation in terms of how its research is known and what its international ranking is and the income it gets from research and all those kind of areas and so that restructure of the university which has gone across the whole university has really had a big impact on the library as well as I said so the library really has put itself in place to to really work with researchers and do as much as they can to help them this is the size of our our population that La Trobe and where we're not a hugely research-intensive university but we still do a lot of research and we have a lot of people to support so we see that as a really important thing for the library to support researchers and we've really done a lot to to make sure we can do that so I guess the new areas for our our future ready agenda for the university were the five research focus areas which are listed there and also the different disciplinary research programs and so being able to engage with those different areas and really support them is seen as a you know a change for the library it's not just about supporting a particular faculty or a particular school it's really supporting researchers who are doing big projects across multi-disciplinary areas and partnerships with other institutions and research income and all those kind of things so that they're really important what our what our approach was in the last couple of years so 20 to 2013 to 2014 initially we had so as I mentioned before we had a digital infrastructure team who were responsible for our repository the ANS projects we'd done up till then in research data management as well as other areas for the library but in terms of research data management was the focus on the repository and publishing data through the repository and it was just done within that digital infrastructure team and all the work we'd done on ANS projects we had a you know we'd done a couple of different ANS projects to publish our data through the repository and published to research data Australia they were really focused on just the team in digital infrastructure working on that and only involving other people from time to time so it was really done as a separate very technical work advocacy going out and talking to academics but it really contained within that area and we felt that it was time to really change that was recognized in 2012 early 2013 that we needed to change that and get faculty librarians to really engage with that work and train them up and and get them up to speed because we saw that that was the the future was a really important thing for them to advocate for that that work that was being done so we we had initially had two faculty librarians who commenced in our area in digital infrastructure they were in there for an eight week period for one day a week and and two librarians came in and really we trained them up initially in what we were doing in the digital infrastructure area so I'll cover what that was a little bit later and then what we thought was then how do we you know do is that a good model we sort of reviewed what we did there had they learned new things could they then share that with their their teams when they went back to their faculty based you know teams that they're working with and could they then have other people coming in and using the same model to learn about what was going on and get them up to speed so that was the model that we did between 2013 and 2014 and we ended up with 10 faculty librarians who went through that program of work through the digital infrastructure area and but one of them Kerry Sullivan was she commenced she was one of the two who originally started in 2013 and she continued on one day a week for the whole period for the whole period of one and a half years and we felt that was really important for that continuity for her to help train up new people as they came in to to the area so when when I was saying each librarian came in for eight weeks it was one day a week for eight weeks so it was really only eight days it sounds like a lot but it's only eight days and they had a lot to do in order to to learn new things and and get up to speed I guess just in terms of our restructuring and where we're sitting and we this is the digital infrastructure area as it was and so we're under Jeff Payne is our associate university librarian and this area in red here that I've got a sort of a dotted red line here is the group that were working on the research data management working with the repository and advocacy with the researchers and and all that sort of area and we and we had the research what we called research data librarians come into our area and work with that group so we had other repository staff down here and we also had other other staff working in different areas so a team of about 11 people at that time but only four people or three people sort of involved in the research data management side and the librarians coming in and then the repository team underneath that and now without following our restructure at the end of last year this is this is what we the new team looks like and this is the research team now so it's a new team completely dedicated to supporting high-degree research students and researchers across the university across all our campuses so the the positions in blue are the new ones and that came into the area and were had to be advertising we recruited those people there are other people who came across in the restructure from different areas or we had to go out and recruit new people and then the other ones in sort of this gray color here where the people already in place dealing with the repository dealing with the research data management and and all those areas and so I moved out of that area and I and Eva fish is our manager of the research team and she manages that new research group that are supporting researchers and I'm her director so our our current approach is really what we've been doing is is forming our new team base and and looking at all the services they provide to researchers to help them deal with their research so it's not all about research data management so there's a lot of dot points there sort of explaining the kinds of things that that group do and but the it really is it has been a fantastic thing that the the library has decided to support researchers in it with a dedicated team who can put all of their efforts into supporting them so they've got a lot to do they're a new team they've got a lot a lot to learn and we and through the restructure we've had only you know one or two people or two people who were involved in that earlier work coming into the digital infrastructure area learning about research data management who've continued on in the research team and others have moved on to the learning and teaching team so the library in terms of our librarians who formerly were faculty librarians and now over either in the learning and teaching team or they're in the research team so it's been quite a shift in terms of how we support our academics and our high degree students so just in terms of what people did when they were trained up in our area the we through an eight week program there was a lot to do as I said before so it was focused on a mentoring approach and self-paced learning as well as meetings with the digital infrastructure team going through training about what the work they're doing and how they support researchers and what their approach has been and looking at new ways of approaching academics and helping so we had a sort of a general introduction as the first thing that came along to the digital infrastructure area and the work we did then after that a mentoring from Kerry Sullivan who was already working in the area one day a week covering the kind of approach we've had and the kinds of conversations we're having with researchers then a self-paced learning approach where each of each of the librarians who came into our area really went through all of the sort of things like and webinars the different different websites they've found different areas that we noted as being really important sites for them to get up to speed with and understand they also went through previous minutes and agendas from meetings through the last year or so that we'd had in terms of our and projects and what we'd done and where we'd got to and speaking with each individual in the digital infrastructure team and then getting getting up to speed with all of that information which which took some time as a steep learning curve it's a bit like saying here's here's a couple of two or three years of knowledge about research data management that we're going to throw at you you know in an in like an eight day period it's a heck of a lot to learn and then once they were confident that they knew something about what they were trying to what we're trying to achieve then going out and contacting their connect their researchers that they have dealt with in in the different faculties and talking to them emailing them initially then following up finding out where they're at helping them you know talking about what we're doing and what we're trying to achieve and finding out if they have research data needs and what they were and publishing through the repository we also had a sort of a general training template that we are a training template that was built up by the librarians to do training of academics and high degree research students about research data management so that was one of the tasks we set as being go and create that and do that and we're going to run some training sessions later you know in the year when people are comfortable get get that whole template working and get all the information and prepared and ready so that each of those librarians could go and deliver those sessions those training sessions and then one of the early tasks that was also undertaken was to create a lib guide on using lib guides to create a research data management lib guide to really sort of record and cover all of all of the sort of key things that we thought were important around research data management and use as a either a training tool or a tool to refer people to as help for training other librarians as they came in to the area all those kind of things we covered things like you know what were the drivers for change what's why we're doing this work now what would have been the drivers what are the funding agencies saying what's the government saying what do we need to be saying to academics to say why why would they even be interested in this so going through the whole background around that and what it means and getting familiar with the guidelines and and those areas so that people understood what they were talking about so this is the kind of thing we covered to do with the open access policies things to do with research data and managing your research data and then we went through also a sort of a very detailed run through what had been done locally in terms of our content and what we've been doing with it so this is a list of some of the things that we really were covering and some of it really quite technical and sometimes maybe too technical I don't know but we wanted to get a sense of you know the work we'd done and the understanding from a repository side and a research data management side and then get the librarians to go away and think about how that meant in terms of them being able to understand that background and being able to sell the story and talk about it with some kind of confidence I guess so things so lots of lots of acronyms lots of new things that people had to know about if they didn't already so things like OAPMH and DC metadata and you know and link data and open access and you know all sorts of things in terms how we we set up we've set up a repository in the work we've done we also looked at you know local issues at Latrobe so our own data management policy and where that's at and what it what it says getting people to understand what that meant and also as I said before in terms of covering you know the ANS and all the advocacy that ANS have done in this area going back and reviewing those the advice they've got on the ANS website and and all the really useful information that ANS produced over the past few years in terms of the repository we also wanted to go through things in detail about what we had in the repository what was driving the content going in what types of information we had in there what formats there in you know how it got in there all those kind of things and getting people really familiar with that and I guess one of the things about the repository also people working in the repository space for a long time has been that the repository is a like a portal for pushing information out externally beyond the university so that there's discovery of that material externally and it drives traffic back to the repository and back to the university and really going through that it's not just a catalog internally of what we have it's also pushing and publicizing that information out and what the impact of that is and and what it means and that was a sort of a different perspective I guess for for some people and then I guess broader issues expanding that out into things like official standards that we use in order to deal with the the data that's in the repository so new concepts like RIFCS and EACCPF issues around maintaining identifiers for people and for records in the repository what data curation means and the managing of the data and the best practice around that and looking at the looking at ANS advice on that looking at webinars and information from there the digital curation center different sites different experiences from other institutions and then covering that whole you know how do we how to where does our data go and what does it really mean in terms of getting it out to research data Australia minting DOIs from data site.org and pushing that information into data citation index and what does that mean all that kind of thing the I guess the other thing around self-paced learning is that the mantra site is something we got everyone to go through there's a lot of information in there but you know a fantastic site for people to really understand a whole lot about research data management so that was something that was great that people really found you know librarians found that as a really useful resource it was easy to use and accessible it was open you know a low barrier to going in there and just actually engaging with that and understanding that site and I think we're all excited about Melbourne University wanting to do a like a local mantra for an Australianized one that'd be a great thing coming along at some point and in terms of capturing what people were learning we also had our librarians created a Google site this is the front page of it which they used as a wiki to really record their own actions and their own thoughts about things and get themselves organized and put useful information on that site so that they could share with their colleagues either when they're in the digital infrastructure area or when they're back at their desk doing their normal day-to-day work it was a really good place where they could you know communicate that between different staff they could really reflect and record you know what was going on and instigate new programs and but it also because it was a Google site it needed to be it was locked down to people who'd created that site two or three or four and as the 10 came in they were invited into that site I think in retrospect I'd be thinking we needed probably more of a broader site for everyone to have access to because not everyone could see what was in there initially this is the lib guide and what it looked like in terms of the site that's been created around research data management that was a you know a good learning instrument for people coming into the research data management area a good guide for librarians to use themselves and has had some engagement and hits from researchers and some you know feedback about it the I guess from from a librarian perspective of the people who worked in in the area they from their point of view they they saw they had a different perspective on on the work being done around research data management and advocacy and what was being done they have a very service oriented mentality really wanting to help help clients with their needs and and that was a really good thing it really brought a different focus to the the research data management work that have been done previously which was more around servicing a few people's needs and doing it getting the technology right and getting getting the systems up and working properly but the librarians really you know were great in terms of making sure everything was documented and understood widely and therefore could go and share that information amongst themselves and be able to advocate properly for you know research data management with their faculties the librarians they helped with the development and structure of the data research data management planning form which we created they provided a lot of advice on the structure of this page and other websites on the university site and on the library site around managing research data they did some a lot of detailed investigation of researcher profiles and identities and using social networking sites using research gate and academia those kind of things they did lots of research into that to how many people were using them and understanding what was being done there they used and they created an email template a standard template to send out to academics around introducing themselves what they were doing and asking if the researcher needed help with their research data that was that was followed up with phone calls and messages and things and meetings and we used a we just used an Excel spreadsheet to track those conversations between ourselves that really was in lieu of having any kind of customer relationship management system which we still don't have and we need a better way of managing that and as I said before they also created you know some classes around demystifying data management which were quite successful and went around to different campuses of the university so we also did a couple of other things we had some re-skilling for research workshops one with Jenny Cameron in May 2013 which was excellent and we got all of the librarians involved in that as well as the people working on research data management and the repository we also did the the subject QUT subject on research support for academic libraries that Jillian Hallam runs as part of the masters program there and that was a really really excellent one as well but we did that in partnership with Caval and we had other people from different universities doing that at the same time that was excellent what worked and what didn't the original approach was very low key so it was low barrier didn't put a lot of pressure on people to do things and get up to speed immediately they were supported through their time in digital infrastructure the period of second was probably too short and it really changed what they were doing you know it it did sometimes and sometimes not and so the new approach around our restructuring is having dedicated staff who can do this so much more long-term approach is working well more of an embedded approach so that they're really working with researchers as much as they can they've got more time to learn the terminology and understand requirements although they probably would say that maybe that's not true with our new structure and new academics coming on board and lots of demands for their time it's still very it's hard to get up to speed with all that and we you know we need more time to train them up so what have the librarians said themselves you can read those screenshots there they you know they found it really good really a really good way of learning new things and and wanted to normalize it's part of the normal normal work that they do and you know a really good way of supporting that work and I guess the thing to understand was it was a natural fit with their skills in terms of what they needed to do so librarians are really great at advocating services and being able to have to advocate what you know what understand what researchers do and how they do research and therefore dealing with research data and research data management isn't such a stretch especially when they're supported by people around them who've been doing that a lot and then what we're going to do in the future is a whole lot more and we've got our research team is very new and so we've got still got a lot more that we have to do with that team and we're looking at skills assessment and identification of gaps and training that they need to do and looking at you know a whole range of services that we're doing and that they can advocate for I might like to acknowledge the the people in in listed in black are the people who initially did the work in the digital infrastructure area came into that area and work and they were the ones who were trained up along with the staff in that area and then the new team is is planning that ongoing training so Eva's leading that with Tracy and Roderick and others in the research team that's all I have so thank you


A film in 3 minutes. Tremors Welcome to Perfection Nevada, where dreams come true. We've got everything a strapping young fella could need to start a new life. Great views, friendly neighbours, hell, we've even got ourselves a convenience store. And as for the wildlife, you can't beat our most beloved attraction. Giant killer, graboids that are actually incredibly dangerous, utterly ruthless and probably should be avoided at all costs. Oh and best not to make any vibrations from this point on, otherwise you might become their next target. Which is exactly what Kevin Bacon and Fred Ward have to contend with in Ron Underwood's superb 1990 horror comedy Tremors. A film that delights as much as it scares, features an excellent premise, a roster of quirky character actors, and set piece after set piece of low budget cinema charm. Originally titled Landsharks, the story's development began when screenwriters SS Wilson and Brent Maddock were working for the US Navy shooting footage on a boulder in the California desert. And then, asking themselves the question, what if there was something that wouldn't let us off this rock? A The Flora's Lava type scenario that inspired both writers to approach friend and director Ron Underwood after the pair sold their first script, 1986's Short Circuit. Underwood, who had experienced shooting documentaries for National Geographic, helped to redesign the subterranean monsters to make them more realistic, turning them into the film's now iconic Graboids. Ancient giant worms designed by the special effects company Amalgamated Dynamics, founded by industry legends Stan Winston, Tom Woodruff, Jr. and Alec Gillis, whose clever use of miniature models and advanced puppetry helped bring the menacing creatures to life and provide much of the film's slapstick comedy. Kevin Bacon, who only agreed to accept the role of down-on-his-luck handyman Valentine McKee due to his then recent marriage and upcoming newborn child, suffered a nervous breakdown prior to the film's release, believing it was a career killer, a view that was strengthened due to Tremors' poor box office receipts. However, in the years since its theatrical release, Tremors would unearth newfound fans thanks to excellent sales on home video and DVD, eventually tripling its original box office gross and leading to seven sequels and counting, a television series and video games, which in turn has made Tremors Bacon's favourite film of his career and the only one he rewatches with fans. Once you see it for yourself, you'll understand why, as it's nigh on impossible not to find the cinematic terrain Tremors inhabits utterly charming. From its lovable supporting characters, clever setups and payoffs, animatronics and at times beautiful cinematography, oh and one of the best uses of a rec room ever shot on celluloid. Still don't believe me? Well then maybe you should pay a visit to perfection and see for yourself. Just watch your step. The ground isn't all what it seems these days.


to stand.  It takes about five to ten minutes and you can sit anywhere because I'll explain what we're going to do shortly and it will involve getting up and moving to other seats. So feel free to stand, sit, grab food, whatever works. And welcome. So I'm Megan Roy. I'm the superintendent of Washington Central. I've met many of you but not all of you. Really excited to have you all here and appreciate you coming out to what we hope is the first of several different and several different kinds of conversations with our community. A couple of logistics. Like I said, oh I think I'm not supposed to stand under there so I don't squeak. I am going to overview what we're going to do, give you a little background information right now. There are people and it sounds like we ran out of, we may be running out of name tags but folks have name tags and if you see red dots on name tags, those are the members of our strategic planning steering committee and we'll introduce them and or have them raise their hands in a second. But they can help during the process as we're having our conversation if you have questions or you're not sure where we are or what the direction is, look to them and they can help you. And we've got some childcare happening down the hall, which you probably know because other than very tiny, I don't see any kids so we should be good. And get food. This is very informal. It's meant to be a conversation so feel free to get up and move around. All right. So a couple things to kind of ground the conversation today. The first piece is our mission and I think people know what this is, but sometimes it's helpful to remember where we are now even as we're gathering information about where we might want to be. So Washington Central exists to nurture and inspire in all students the passion, creativity and power to contribute to their local and global communities. Some more grounding work that we would like to have everyone have in their minds as they talk is these are three pillars that kind of inform everything we do at Washington Central.  But all of our work falls under one of these three things. And that's academic achievement, safe and healthy schools and humanity and justice. So we want those things in mind as we do our work. And why are we here tonight? So tonight's conversation is part of our strategic planning process. These are some words that you don't have to read it. But the purpose of this, this has lots of phases, but the entire thing is a multi year process. But we are looking to have a visioning and strategic planning process that really emphasizes engagement and equity and lifting as many voices as we can, which is why you'll hear me say lots that this is not the only event and it's not the only type of event because this is wonderful. And it's not the only way that we can get voices from our communities. And so we'll talk about that a little bit. But the goal is to create unity within our system and really have a common understanding of what you all think our students should have as part of our educational system. Here are steering committee members. Do you want to raise your hands? Those of you that are here. Thank you. This is a great group. We've had some really great conversations and we will continue to have great conversations and we learn from each one of them and we'll learn from this event too. So really appreciative of those who are giving up time to do this. And this is just a little bit to ground you. This is our timeline and process, right? Well, we're here. But I'll go back. This first phase is really about a big funnel community engagement, kind of hearing from folks about really high level core values. What do we want for our students? This will move into a really iterative process of processing that, drilling it down, sending it back out for feedback, engaging with the community, getting to some strategic goals. So this is kind of the long term process. This is where we are now. And just as a teaser, and we're, we are still working on making sure we communicate this out really well. We'll get better at that as we go. We know that not everyone can come to an event. So we will have different versions of this. We will also, every question that you're about to react to is also going to exist in a survey form that we will bring to our communities. These are some school based events between now and the end of the year that we will bring it to. And there will be community based events that, that are community events that we'll drop into. We'll also have conversations about do we replicate this in a virtual format? Do we, do we have another event like this? So our steering committee will be debriefing after tonight. But this is some, some information. So as you were walking in and there's lots written on things, which is great, you were kind of reflecting on your core beliefs. So what are core beliefs? What's a vision? I mean, people know what those words mean, but this is really about something that will give us a compass or a North Star to help guide our decisions, guide our budget decisions, guide the work of what we're doing as we move forward. And it's really important to be able to ground everything we do in what our community believes we should be doing. So part of our process is then turning that vision into action. So we'll be synthesizing words into a vision and core beliefs. We will send those back out to the community in multiple formats. And then we will continue to iterate. We know that we have opportunities and we have challenges in head. These, if for many of you, knowing these are why you're here. Others are new to the process and you're here to engage in it. But our challenge moving forward is figure out how do we stay true to our core values and vision. Build on what's already happening in our schools that's going really well. Build on what we're already doing. But also respond to changing conditions. We don't have a choice about our changing conditions. We know we have enrollment declining. We know that our communities are facing the same economic realities that everyone else in the state is. And there are some things that have nothing to do with us about how education is funding, funding that we've had and we won't have anymore. Those are things that we're going to have to figure out how to react to. So when we respond to those conditions we want to be able to do that keeping our visions and values in mind. And it's important to know that we're starting this way because we don't have preconceived notions about where we'll end up. We know we've got some things we have to respond to. But we want to know what the community wants for our schools. So today we want your ideas. Okay. I'm going to pause briefly. Are there any burning questions that someone needs to ask because they don't understand and they won't be able to move forward. Yes. Positive things that are that we don't have real control over that might change. If you go back to slide. You have two slides. You have defining enrollment, economic realities and funding changes. Are there positive things that could go in that bottom bucket that we need to respond to? Probably. Do you have an example in your head? That's a genuine question. Yeah. Of course. These are the conditions that we've been talking a lot about recently because they impact us and they impact us soon. But absolutely the educational landscape in Vermont I would call a positive condition. I would love this. That's a great question also to weave into the conversation we're about to have. Okay. Here's what we're going to do. At some point tonight you will go to four different tables. Every table has the same set of questions and you'll do the same thing which means it doesn't matter where you start. And the tables that look less cluttered so not this one and not this one are the ones that have the set of questions on them. The questions will also be up here and people with red dots will walk around to make sure you know what we're doing. And although I think we fit nicely we can bring down another table if it feels too crowded. I think that we're okay right now. But you're going to go to four different tables. You're going to get to have a conversation about a specific question and take notes on that conversation. You'll have about seven minutes at each station. I will time us and I'll give you a one minute warning. Take notes that capture the themes and highlights of your conversation. If you happen to be at a table and nobody likes to take notes someone with a red dot will take notes. Couple things as we have these conversations. We call them working agreements most of the time when you sit at a meeting. We have a set of norms. We've tried to kind of modify this for this kind of event. To the extent that you can. Small tables hopefully makes for a manageable comfortable conversation but try to make space for all voices. Not everyone loves talking in this kind of setting. Try to stay focused on the question. There is a reason why these questions are the ones. It doesn't mean that you can't jot down other things that you want us to know. And actually I can put up a parking lot chart paper. So if you really want to make sure that we get to something later we'll make that happen. But try to stay focused on the question during the time that you're at the table. Be present to the extent that you can. And then just remember that we're this is a big setting. We're we're being recorded so people can hear a portion of our conversations later. So just know that as you have conversations you might have stories you want to share. Just be aware. We can't. This is not necessarily a confidential space so. Okay. So the first round. Says stay where you are unless you feel like your table is very full and you need to move. But the first round is the first question. It should be the top of your kind of extra big size paper. Yep. It says hopes and dreams. What are the hopes and dreams that our community has for our young people. And I'm going to set a timer and you can start your conversation. Yeah. Yeah. I think form. Formative connections. With educators. Absolutely. It's good. We elected you. Prepare for the next. With that. Add on to that. Like. To be ready for the next steps. Not just like next. Know your options. A valuable part of the community. And some of that might be to. Sort of have the opportunity for individual exploration. I'm out of the feeling next steps. These are opportunities for exploration. Individual. Figure out what that is. I want them to be valued. I love seeing how other people like it's like what their moves are with their babies. Like some working and positive models for the on place. Resolute. You had some. Respectful of one another. Especially. I think that's. Especially. It's easy to be respectful of people. I'm coming out. I think for me it's like a safe. Don't. So you said a safe support. Inclusive. Strength. Oh, interesting. Yeah. Yeah. He works for Microsoft. I just want to say one thing about him. Sustainability. I don't think we're going to be able to do a program. Because he was supported here when he went broke with his love. He had connections through... He might, if I'm out, just do a great job. He's going to be better if I get him to get out. You did? You got him? Yeah. Now he just goes to do things. I still go to school here. I know that if they did, the hopes and dreams I have of them are really strong and confident. Help them get that. It's not necessarily that they have to be great at it. Right. Comfortable in their skin. There's a piece of me that's comfortable in their skin so that they're willing to take reasonable risks and be stuck. I'm not sure how to phrase that. I think willing to take risks and end life. Yeah. Yeah. Whatever they might be. Right. I think anemic, social. Right. Yeah. So that would be to never get too conflict or... Two-minute warning here. Part of salient. The kind of bouncing back. Yeah. We do. Yes. I think nobody knows how to really spell it. Well, it looks like re-silence. There's also a piece of accountability. Yeah. I have to take it off. What's that? Can she say... So, I'm back. Yeah, okay. Thank you, Becca, for bringing us back. What about... There will also be some joy in our doors. Yeah. Would you like to be right for a bit so you don't use the time? No, it's okay. Can you add... Well, no, just add this to the joy. The fun. Yeah. I know you two can read it, right? Oh, yeah.  Helping our kids wade through this misinformation. Become informed citizens and voters. Become smart digital citizens and consumers. Also helping kids have a foundation in history. Help the kids to feel a sense of belonging and connection. All passions, expressions, and your sense of value and equality. Stop! Come to school open-minded, excited to learn and realize that you're part of a larger community so that you become like learners and learn to take risks. Perhaps they could have more opportunity to engage in real-world opportunities. Okay, we are about to move. In order to learn more human skills and identify what those skills are. So you're going to move to a new table. Thank you for that. This is always hard. Nobody wants to stop the conversation. Remember when you move to a new table, the goal is to sit with a different group of people. So try not to move as a group. Try to filter yourselves around. Get settled, and then I'll read the question. Yes. No, sorry. Everybody is going to the same second question. You totally could. Okay, so the next question. And you've already done a little bit of this with the exercises you came in. But what are the core values that should guide Washington Central as we make important decisions? It is a mouthful. Core values. Shadow. No inclusivity. I'd like to start with kindness always. Kindness. Yes. We got Zach. Thanks. We can do multiple choice. I'm thinking of something in terms of student centeredness. Okay. What about you? You don't want to leave your voice out either? I think integrity is important right now in the world. What does matter work? Integrity. Yes. Good one. Which one? Empathetic empathy. Oh, yes. That's huge. She's a queen. One side is Elena Abilar and one is Brené. She does not have it. I was looking and I was like is it not a value? Is it considered a quality? We've got inclusivity, kindness, culture, community, student centeredness, integrity and integrity. Core values. There's something about being able to freedom dream the future. Freedom to dream? Well, to dream into the future. Freedom to dream into the future? I think that's what I want to say. We have to start thinking about... Say more. Well, we can't be stuck in the standards that are met. They're kind of irrelevant to most of our kids. So what's next? Willingness to change? Is that related? Growth mindset right there? My big number word. Freedom dreaming and being really creative is thinking beyond all the boxes. So that families and friends are included in the education. Hi, oh yeah, let's get it. Better looking than ever, right? Jody. So the interconnectivity is that... You like that? You can make up whatever you want about what I said. Yes, you're right. I got distracted. I need a new tag. I think you need one. I'll make a messain at one point. Okay. Action, relationships, belonging. How may we elaborate on this? I think I would suggest along that kind of creative bouquet is embedding in that what stages of development. So as we think about potential shifting, or if that's on the table, where are these students passionate about the talent she's been tapping for? Or are they particularly a spot in that field? And again, ask ourselves the question. Why do we keep this group together? How does this work? Are we doing all of this for budgetary reasons? This is your two-minute morning. What are you saying that is the root of this potential shift? I think we need to interrogate. What else? I like to add sustainability. Not just of environmental, but of all aspects. We need to set it up in a way that works for everybody from the students to the taxpayers. That's one of the positives. When somebody asks or something positive that's on this list, we have an opportunity here to create something else. When I think of a question that we should always come back to is how does this decision have for students? When you're talking about sustainability and even if you look at it from a investment in terms of decision-making clarity or transparency it's really important. You're tired. Especially decision-making process. And to get a little bit nitty-gritty, making sure that it's in laypersons. Everything is in laypersons terms. So when you're thinking about every member of a community being truly able to make decisions, I think educators can understandably use it to master education. We've had lots of conversations about that this year already and actually the way these questions actually work really hard to break them down. I have a notebook of jargon. You can So we're going to move to a new table. Again, make your group up. There is a little prompt that says try to go two tables away from where you are now, but nobody's going to monitor that. I'm sorry. I'm sorry. I want to have a chat. I have a form of teacher. Okay. Okay, a question for this round. What are the answers to some of the other ones probably in the answers? This one is what skills or qualities are most important for our students? Skills and qualities. I have another one. That says credible, right, because I think first we have to start. Passion. Passion. It's not a right passion, but I've heard Yeah, yeah, yeah. Say the ability to work hard, the ability to actually put in work and how you... Perseverance, persistence industrialism. Just good old fashioned literacy. Literacy, you know, literature. Literacy. Literacy. I mean, right, how nitty gritty do we want to have specific... Skills and qualities. Right, skills and qualities. Civic engagement. Civic engagement. And locus of control. Like, no, and they can make a difference. Like that. Instead of a... Instead of something like a jargon. I don't know. I feel like... I'm just gonna write, because I think our curiosity... I would say graceful. We're kind of in a moment in history where we need to teach kids or re-teach them the concept of how to have grace in your life. We have someone to benefit and endow. I like that. Let's see, self-awareness. You've met every middle schooler. Does anyone hear, like, on the floor? And this is the table? I'm not on the floor. Okay. This is the table. This time, I bet there's time. Oh, man. If you want to encapsulate that in something, as we did in our communication or just in academics. Yeah, I mean... I'm part of the community too, so anyway. But it's like... it's... to find joy in learning, right? I really want to be a learner. I want to be independent of learning. It's curiosity and a level of... How do we... It's fun. The what-it... skill, it's the what-it. Which is more of a... it's really, um... different things and analytics. But... being able to read That's the skill. How do we, um... talk about facing the digital realm versus the real realm? Yeah, there's something enormous there that's already here, actually. Two more minutes for this question. Two more minutes. Fake intense. Yeah, relationship. And how kids are kind of swallowed. They don't hear the birds because they're too busy listening to them. You know, and how to keep them grounded. I mean, we're... we're always going to have the digital stuff, but how do we get the kids to understand? I think... the earth to walk on and with... There's something else. I don't know if you've got the language or how to deal with this, man.  So you need to... bring to our kids. Give me... I'm deleting all my stories. Well... That doesn't make them go away. But how do you encourage kids to... not take the easy way at the end? I don't just take the easy way. You know? I'll teach them how to use it in a responsible way. I mean, there are pieces of it as an adult who struggles with spelling that I fully embraced. That are tools now that I didn't have before. And so I'll use those responsibly. And ethically. Oh, no. I mean, there's other more AI tools that are helpful for people who have different levels of learning. And that's true much of technology where kids with autism can really fly, right? So... This actually has us returning to your starting table. So you do actually get to sit with that original group again. Okay, so your last question is how will our young people know that Washington Central cares for and about them? Returrent, yes. And we can be flexible if people didn't. They'll be challenged. No. Right now, I'm Stephen Bell. I don't know the principle here. All right, It's hard to believe that they weren't at the beginning. They did assume a lot of somebody in the crowd and they couldn't collect that. I think it was, I think so. Well, I'm trying. Don't have your newsman at school. Yeah. But there's a lot that goes into that, I guess. Advocating. Give them opportunities. This is a challenge. You're welcome. You know, they see Steven like joyful. You know what I mean? I mean, they're just using an example, but like, oh, he likes, he wants to be here. You know. Place of joy. I'm going to use when Jamie, I'm going to subconscious them down. Do you know what I mean? When Jamie comes around and helps them, they either get back to class or it helps them out at the moment. Right? When adults help. Yeah. Conscious. Conscious. Conscious. Conscious. Conscious. Conscious. Conscious. Thinking about like, fill like, they can actually like achieve academically. They can push themselves and academically. It's all hard to solve with it. Everything comes back to, like, when you talk to support them and, right, when those kids see themselves achieve that because of what is something you need to help support you. Yeah, which I would say is, has to be the first five hours of it. It's with celebrations or celebrating learning. We might think that and believe it, but it's not shown through. The way that we enforce this. You can be quiet like those out-of-boys, right? They're very specific cranes. They talk about that when you're reading. It's just like, good job. You work really hard on it. And that comes from the adults in our system. It provides specific feedback. Specific feedback, yeah. I was going to say, we asked them how they're doing. I just heard them taking Shelley's class, right? We just had that conversation. Someone had asked me how I was doing. Nobody ever asked me. Maybe I was just coming back at the step of celebrating different ways of getting to outcomes. Celebrating different ways of problem-solving. I think also, let me know, it's been going too fast. I think being willing to do this trick is willing to change, really flexible. To be in chains of bodies, floors. And maybe it's following the math, before the system to admit something. Two more minutes. Two minutes. We're going to be really angry, we're really going to speak out against something. Opportunities they want. Oh, that's a good move. Despite needs. With respect. So, it's like, not just financial, but also neurodiversity, and other kinds of access issues. And so I want to do this. Pretty remote. But they just scroll down their windows at our beginning, and I'll take you home. He said maybe where my driveway was and everything. But he's the kind of kid that could sit next to you and not know your name. It just didn't matter to him. You were fine, you talked to him. And I didn't, it was never the stranger danger or anything. If they're on our road, they must be saved. They made it here. Yeah. I don't really reach out. Okay, you want to wrap up? Wrap up. This part of the discussion. I'm going to just add maybe that. So, I'm going to give just a couple of logistics. Then I'm going to pass it on to Floor for the closing activity. So again, the reason I'm showing this slide again is mostly to emphasize as you go back out in the community and remind people that this is not the only event, that we will have an opportunity to ask people those same questions, and we'll have an opportunity to ask people different questions as the process evolves. And for those of you that have kids in schools, these are school-based, events that we will have a table at and an opportunity to respond to this same set of questions. And again, we'll sort of have opportunities in the community for that as well. And I'll pass it on to Floor. Thank you, Megan. So, well, first, thank you, everybody. It's so exciting to see so many people here that showed up and are willing to have this conversation with us. We have two questions. We put some post-its on the table, and we are very excited to see how many people post-its on the table, and we are wondering two things. First, in the post-it, if you would want to write, what did you hear tonight that leaves you encouraged or inspired? And then on your way out, you're going to paste that on that piece of paper there. Or you can also give us some feedback on what can we do better. So either one. And then in the last one, who do you want to bring into this conversation if you were in mind turning the paper in the back of the questions and writing, if you have any suggestions to people that we should make sure to reach out directly to the next one, that would be wonderful. Any questions? Yeah. It's double-sided. You can write on the back of the number. Thank you. And if you run out of post-its, there are markers. You can write your thoughts, or you can consolidate your thoughts onto post-its. But the goal is just give us some feedback on your way out. And really, we appreciate you being here. Oh, and there's still a little food, so please on your way out, get some food. Yeah. Well, both. It's a difficult one. Yeah. I think those are important voices. What is happening? Do you think you're offering that? Do you think the voices of students being a graduate here would be helpful, and would not do a different... I would love to... Yeah. Huge. There are a lot of former students working in the living room. Yeah. So, do you know some phone folks? Yeah, we're thinking of them. I don't know if you can guess. Matt Levin? Yeah. And then Erica and Brynn Hare. What's the last one? Erica and Brynn, the R-Y-N-S-E-R-H-J-R-E. Do you have some people letting them know why you think this is important? Do you know why you're here? Do you think it's important to be valuable? If you're trying to have deeper conversations, you don't have to be 100%. I ran for student rep last summer and I would be on probably a good day and probably talk, knock on 20 doors and maybe have two to three good conversations, but they could be really good conversations. You know, you get a decent... You don't need to... You may be going after quality, not quantity. And if you have quality, then you're likely to get their relatives and their friends and neighbors. One of the things that I'm concerned about and I don't know... I don't know the specifics of who's in the room, they don't know who's in the room, but the people who show up to these things tend to be disproportionately higher income. Very good. It's good to have a child. Yep. Option. I don't know what it is, but it's just the size of your mother and your elementary school, so it's pretty easy to post them. I'm happy to have a conversation with them. I have a question and that's sticking out next to us. Yeah. Yeah, these conversations can be... Probably if you learn open to it, you need to be asked maybe twice. Everyone volunteer, you're retired. I'm a persuader. But how about the people who feel like they've already done this before? Personal invitation. People who have kids. They're so packed together. Yeah. Maybe five to five. So, how do we get them? Thank you. That's what I said. No. I'm on. Yeah. So, last steps, when you're done this and this below on that table, again, your sticky notes on the poster sheet and grab food on your way out. So, I'm very serious about wanting to have food and how it's not just the two of you, it's like the album of some sort. I think what needs to happen is what's going to happen in the future. You know, like, it's true. I came from a serious opinion that it wasn't open to anybody, but I think you're going to get. That's how you're going to get somebody comfortable in. And to honestly put it to my individual, it's very convenient. I supported four members. We're going to have a round of money party. And, We're going to do some at first. I practiced.  We talked about... I walk by your building. The transfer station. Do you have to go? No. I think they used to do things like that, and they're used to being able to chill out, like having questions, like a board where you can just like on and out. It's laptops, but not computers. Right, laptops. So just like the chalks on the board. It's... It is such a lie. No, I know, I know. It's like you're going back, come on, be clear? Yeah. I'm not just... I work with Lister.  Like I was like taking a shower while we were at work. And they'll be back later. I'm moving it down. Yeah, I know. We're talking about the art sense of the school. I was just like, yes, I'm like... I've been putting in like a new security system. I worked with Lister. I worked with Duke. Right. Duke. Yeah, I was in the... Now I'm under repair. So I already have some stuff. I work with Lister. Because I do support. So I like to work with many things. Okay. Yeah. Yeah. So I love Lister. Yeah. I used to work with Lister. Yeah. I used to work with Lister. Oh, wait. Oh, wait. Yeah. Yeah. I love Lister. Yeah. Lister, really. Well, yeah, I love Lister. Yeah. I know, I like it. I hope so. Yeah, I love Lister. Yeah. I've heard about you. Yeah. It's just your decision. Yeah. I'm just a fan. I'm not a fan of Lister. That's true. It's for Lister. Yeah. I'm just a fan. Oh, thank you so much. Thank you so much. Thank you so much. And thank you. One more time.