Dr. Eric Gordon is a professor of civic media at Emerson College, director of the engagement lab and assistant dean of civic partnerships in the School of the Arts. He is also a research affiliate in comparative media studies at MIT. His current research focuses on emerging value-based governance structures in the smart city and the ethics of data access and sharing. Additionally, for the last 10 years, Professor Gordon has explored how game systems and playful processes can augment traditional modes of civic participation. He has served as an expert advisor for local and national governance as well as NGOs around the world, designing responsive processes that help organizations transform to meet their stated values. He has created over a dozen games for public sector use and advised organizations on how to build their own inclusive and meaningful processes. He has to author two books about media and cities, The Urban Spectator and Netlocality, and is also the editor of civic media technology design practice published by the MIT Press in 2016, as well as the Ludics Play as Humanistic Inquiry. His most recent monograph, Meaningful Indeficiencies, Civic Design in an Age of Digital Expediency, published by Oxford University Press in 2020, examines practices in government, journalism, and NGOs that reimagine civic innovation beyond efficiency. Professor Gordon's talk today is entitled Deep Listening, Post-Engagement in the Smart City. In this talk, Professor Gordon will introduce the concept of deep listening as an alternative paradigm to engagement. Deep listening implies flexibility, reciprocity, and accountability, and it's being actively imagined through the use of novel tools and methods for governance. I'm sure it's going to be an insightful talk on the paradigm of engagement that can effectively respond to community needs and expectations in contemporary today. So with that, Professor Gordon, if you're ready, I'll pass things over to you now. Thanks so much, Elena, and thanks everyone for coming. It's a pleasure to be here and to share some of my current research with you today. So just give me a moment to share my screen. Okay. Is that visible for everyone? Yeah. Okay. Great. I asked and I wait for reply now because I have actually gone 15 minutes into a lecture with my slides not showing because I haven't asked. So I've gotten better at making sure, confirming that my slides are on the screen. So as Elena mentioned, I'm going to be talking about what I'm calling deep listening. I'll explain that in more detail as we go and how we think about this in terms of the smart city or future cities. So Elena has already mentioned a little bit about my work and I just want to, I'm just going to introduce myself as both a scholar and a practitioner. And I think that that connection is really important and it will come out and what I'm saying today. And I think about my scholarship always at the intersection of practice and I hope that it is explicitly informed by that nuance of practice. My research focuses on urban innovation and public engagement with a specific focus on how public institutions are incorporating new data practices and their efforts to become more responsive to constituents. I've done this in a number of ways. These are some of my books that was mentioned. But again, I also do a lot of collaborative design work, a lot of work with multiple partners that I'll talk some about as I go on today. I want to start by providing some context with this idea of the smart city, which is a term I don't particularly love, but I think it's important to address. Smart cities is a designation that has become a marketing catchphrase that positions competing municipalities in the global marketplace of innovation and creative economies. The label tends to invoke images of responsive technologies and efficient infrastructure. And there are global smart cities competitions and mayors around the world are eager to put this label on their place. But the standard definition of the smart city does not typically include communication infrastructure. In other words, how institutions communicate with constituents or communities. Myron Bolivar argued that the smartness of a city refers to its ability to attract human capital and to mobilize this human capital in collaborations between the various organized and individual actors through the use of information and communication technologies. So they identify four ideal, typical conceptualizations of smart city governance. One, government of a smart city, so the actual government organization itself, smart decision making, smart administration, and number four, smart urban collaboration. So smart urban collaboration refers to the notion that what makes a city smart is the ability for local governments to partner with other sectors, especially community groups, both formal and nonprofits and informal mutual aid to achieve mutually beneficial ends. This form of smartness is the application of desires for smart physical infrastructure where environments are responsive to individual patterns to all the way to smart social infrastructure where institutions are responsive to individual patterns. So this is not just a matter of logistic superiority where digital media aids and smart decisions based on the collection analysis and sharing of data. It actually requires the cultivation of trust in non-human systems to address or repair trust in human systems. So for example, if we imagine dumb governance to look something like this and smart governance to look like this, I think we're missing the point. Simply taking more in does not necessarily lead to smart decisions or more urgently mutual trust in the intentions of those decisions. And simply being creative in the way information is taken in does not address the problem. So more creative methods of engagement, more dynamic interactions, a greater capacity to collect data still largely leads to decisions being made in the same way. This is a screenshot from a project that I created back in 2011 called Community Planet that used playful imagery and sort of game-based mechanics to cultivate a three-week planning process within cities. And the reality is, as I reflect back on this project, that it was able to collect a significant amount of data as these projects go. But ultimately the institution that was on the receiving end of that data was really no smarter in the making, it was just more data. And so the challenge is that institutions lack the capacity to listen regardless of how much they ask us to speak. So while most of smart city discourse is focused on infrastructure improvements like autonomous vehicles, smart thermostats, responsive lighting and other augmentations to the physical public realm, there is an emerging desire to see responsive machine augmented governance that can reshape how people interact with institutions. City governments are encountering constituencies that have become used to the networking capacity of digital media and have lost faith in traditional institutions. As such, existing modes of engagement where institutions present the plan to those impacted by it and gather feedback only to execute the plan with minimal changes have proven insufficient for those already disenchanted with public institutions and whose diverse interests and needs require a more nimble and responsive decision-making structure. Institutions mostly lack the tools, training, and experience to listen to people in ways that their voices impact, in the way that their voices impact how decisions get made, implemented, and assessed. Barriers of low trust and disrespect between community and governing institutions and misalignment of priorities lead to antagonism and competition as opposed to the collaboration necessary to meet diverse and critical needs. City governments around the world know they need to respond, but are struggling to match practice with intentions. Many have created engagement teams or entire offices focused on constituent relations, but these organizational fixes often come up short as they seemingly just rearrange the deck chairs on the Titanic. More bureaucracy to manage bad systems just leads to more stable bad systems. Increasingly, cities are looking to novel uses of data and algorithmic decision-making technologies to repair and restore trust in systems. In many cases, the processing and analyzing of large data sets provides agency to government workers to transform value structures of organizations without changing the org chart. So mid-level bureaucrats are able to question and or shift policy by looking at data differently, disaggregating data sets to look at racial or economic disparities, and even directing resources to people in places once overlooked by blunt engagement instruments. In these efforts, there is a shift from standard practices of public engagement to what I call deep listening, or a desire for a responsive and more nimble system of governance that take in and respond to complexity. So I want to show you an example of a platform called Desedim, it's an open-source platform that first came out of Spain. Here's a quick video. We spend our day on social networks, seeking approval from others, or reinforcing our ideas about how badly the world works. What if instead of complaining, we could connect with different opinions, discuss common concerns and reach binding agreements to make real changes in our lives? This is Desedim, a political network, a platform for democratic participation. Easy to use for any group of people from the smallest to the largest.  Citizens can use Desedim to provide different proposals on where to spend money, evaluate their viability and vote for them. In addition, it allows you to track in real time how the selected projects are carried out. If on the other hand you're a group of people who want to self-organize as an NGO, a cooperative, association or collective, Desedim also helps you. It makes it easy for you to get in touch, share information, schedule meetings and manage assemblies to achieve your common goal. The platform adapts to your needs. Desedim changes and improves. It grows with contributions from the community through its free open source code. It's a political network to make decisions in a free and secure manner. It respects people's privacy. Desedim is a techno political tool to build the democracy of the future. Democracy never felt so real. Okay, so I'm showing you a promotional video of Desedim. I'm not going to go into too much detail about the platform and its uses, but I do want to focus on its rhetorical position and I'll reference it again a little later on in my talk. But what's sought after in Desedim and other similar initiatives is really this concept of procedural justice or recognizing and respecting cultural differences and removing procedural obstacles that prevent marginalized groups from meaningfully participating in decisions that affect their property, well-being and or risk. Research has demonstrated that people care as much about how disputes are resolved is about the outcomes they achieve. So when planning processes are perceived to be unfair or unjust, the likelihood of maladaptive outcomes is significantly increased. And I think what's important about Desedim in this case is that it functions extra institutionally and it works sometimes within government, but most often outside of government in some way that connects to it and the idea of how it collects data is just as important as the data that it collects. Oops. We spend our day on social networks. I do not want to watch that again. Okay, so it's clearly present in the in the Desedim rhetoric. This does not only emerge from within government but as a result of collaborative mechanisms across sectors. So because of barriers of trust and existing institutional structures and a mismatch of priorities between those of institutions and those of communities, it is clear that the sole technical capacity of institutions is not sufficient to govern. Resources need to be devoted to creating collaborative governance structures which prioritize local values through multi-sectoral decision-making arrangements and then have the ability to translate between community and institutional context. And this is where something like Desedim comes in actually as a as a machine aid in allowing that collaboration to take place. Ansel and Gash define collaborative governance as an arrangement where the government directly engages with other stakeholders in a collective decision-making process with a specific setting and rules that aims towards consensus that is deliberative and that aims to make or implement public policy or manage public programs or assets. The research around collaborative governance structures and mechanisms is really uneven in the sense that it's largely case study-based and anecdotal and not all that lasting. And I think only recently have we seen an increase in research that looks at collaborative governance structures that are seriously taking into consideration the role that data and technology play in enabling those structures. So this leads me to my main point which is that city governance is in transition and the engagement paradigm structured around mostly one-way communication between public sector institution and community is eroding for the reasons I've discussed. And there is a movement towards what I call deep listening where the public sector institution is charged with listening to a diverse group of stakeholders. And this manifests in different ways from collaborative governance structures that make values and intentions explicit to all the way to tech mediated platforms that draw clear lines between input and output. For example, the social media analytics effort in Philadelphia called Zen City that gathers data from social media platforms and brings it into governmental processes. And so this is an example of governmental listening into places where people are to bring it into government decision-making and an attempt to create through lines between the data collected within social media and government decision-making. This effort is relatively new in the city of Philadelphia. And so there's not a whole lot about it yet. But it's interesting to see as it progresses into its post-beta phase. Another example that I'll share with you is a very different example of listening is a project called Real Talk for Change in Boston. And this project has created a robust community dialogue platform that is partially mediated by AI. So I'm going to show you another quick video to introduce this project. After a historic year, the local elections in November have the potential to produce historic change. But all too often the conversation is dominated by opinion polls of likely voters, as well as sensational stories on social media that distract from real problems. The truth is underserved communities with critical perspectives on the issues are often underheard in civic spaces. Real Talk for Change wants to amplify these voices by turning conversations into real impact. Here's how it works. A team of trusted community leaders will host small group conversations in priority neighborhoods across the city. You'll share your hopes, concerns, and experiences of living in Boston, with the option to state your name or keep it anonymous. These conversations are recorded, transcribed, and entered into our online platform. Our team will use online analysis tools to sort through these conversations, pull out patterns, themes, and insights, and summarize the findings on a public dashboard. We're inviting candidates for mayor and city council to use the dashboard to better understand the lived experience and perspectives of folks in Boston. We'll also use the dashboard to recommend questions to the media for public debates and interviews with candidates. And it's all public, so you can watch your voice influence decision makers more directly than ever before, and learn from one another through cross-community listening. Together, we can constructively shape the 2021 Boston election cycle and build a better, more equitable city. Join the conversation, share your experience, inspire a better Boston. Okay, so what I want to point out about real talk, really interesting project coming out of MIT, this exists outside of city government. I think it's really important. It relies on certain data analytic technology to do a first pass on thematic analysis, which is also really important. And something I want to talk about a little bit later is the role that in this case, the role that machines play in building trust, or at least the perceived ability to build trust. And so in this case, that there is a robust human infrastructure that's behind the collection and facilitation of the conversations. And then the machine pass that is talked about really as a way of reducing bias, which is interesting in the way that the data gets formed. So a couple things that are better, I want to point out here, different than Zen City, located within government, a kind of listening that is more akin to social media surveillance, bringing that back to government decision making in some sort of clear way. So sort of go where they are kind of idea brought to the surface with that technology versus something like real talk for change, which is a very different kind of listening that is done in more of a collaborative governance structure that is in collaboration with government, but not run by government, and actually undertakes the listening procedure quite differently, which we'll continue to talk about. After a historic year, the local elections in November have the... Sorry, I don't know why that keeps happening. Okay. So I want to talk about... I'm going to break this up into five different components of deep listening. And so with these examples in mind, there are five components that I'm going to talk about and that I want to highlight as the emergent, practical and discursive trends in urban governance. So they include knowledge sharing, so the mutually agreed upon protocols for data production and use, holding space, or the way in which spaces are co-created for institutional actors and communities to exchange and learn from each other. The production and sharing of civic imaginaries where local knowledge and community values are brought together. Sense making with the diversity of perspectives, so how institutions essentially make sense of the data that's brought in. And then finally, evaluation and monitoring, which is to assure accountability and assess quality of information. So let me go... I'm going to go one by one and talk about each of these components. First knowledge sharing. And I just want to make clear that I'm saying that these five components are part of this both discursive and practical shift towards a listening paradigm within government structures. And they're being worked out in different ways that I want to talk about. So data is not neutral, as we know. Who collects the data? Who owns the data? How the data is collected? Impact whether data is trusted and by whom? Put bluntly, data collected by institutions differs from the data potentially collected by communities. So smart governments see their role as the collector and processor of data concerning demand for public goods and services, so that allocation of resources can be rationalized, quantified, and addressed through efficiency-focused tools and processes. So as Shannon Maydern argues, smart urbanism is an understanding of what we know about a city and of what is worth knowing about it, which systematically excludes other forms of local place-based intelligences and knowledge institutions. Or as Dignacio and Klein argue, normative decisions behind what data is collected, how it is displayed, its accountability, and how it is made authoritative are crucial. So in this sense, data feminism, a viewpoint that aims to achieve the most complete knowledge, to the synthesizing of multiple perspectives with priority given to local indigenous and experiential ways of knowing, presents a valuable viewpoint. So for institutions to collaborate with communities in data sharing, there's a need for data sharing protocols, social and technical rules, that establish equitable data governance. So as an example of this, we can look to the care principles for indigenous data governance from the International Indigenous Data Sovereignty Interest Group, which is a network of indigenous peoples, scholars, nonprofits, organizations, and governments. In the context of smart cities, this means ensuring that data and information about and from communities are controlled by communities. And while there are many existing resources for official government data like government data portals, there are few equivalent trusted sources for community-generated data. And this leads to standstills, where community organizations are unwilling to offer up their data for fear that it will be misrepresented or misused. One community leader told me recently that she was sick and tired of getting a pat on the back by government saying, great job, thanks for the stories. And then proceeding to take all the credit and then the government proceeding to take all the credit for innovation while leaving her doing the same work she does every day. And so the ownership that communities feel over their data has to do with ownership of their own agency. And when there's not the protocols that are in place that allow for the trusted sharing of that data, as I said before, it leads to standstills. And as cities are sort of moving to this listening paradigm, figuring that out has become essential. So the second piece we call, I call holding space. And so while some data is generated passively, data used in governance is often actively produced. So town halls are traditional instruments used to collect data and they depend on who shows up. And the data is typically not formally recorded and decisions are untraceable. And of course, there is a wide range of town hall forums influenced by the environment, the host, the procedures, et cetera. But there is a growing awareness among city stakeholders at the model of data collection where everything is framed in institutional terms. So city projects, city zoning, city policy requires feedback that can inform city procedures is steadily chipping away at trust. So ultimately, the institution continually and perpetually frames the conversation that the trust that communities have in institutions continually erode. So instead structures of collaborative governance where community gets to frame the questions and government participates is prompting new models of assembly. So I'm going to share another video from a project that I was a part of called the People's Collaborative Governance Network in Boston. And this was focused on building the spaces for multiple stakeholders to share power. So one more video here. Across the country and right here in Boston, communities are facing growing disparities in housing, wealth, educational outcomes, and health. Most often as a result of systemic racism. Local governments and community-led efforts to address these wicked problems continue to come up short. But what if residents and government collaborated to create new tools, engagement practices, and policies to tackle these issues together? What would it look like if government recognized that it must share power with the people to have a more equitable society? Leadership that prioritized relationships, storytelling, meeting people where they're at. Boston needs relational leadership. It needs leadership that is centering relationships at every forefront. PCGN is cultivating a network of collaborative leadership and design skills to solve the city's most pressing and public problems. We are reimagining how local governments can partner with communities to transform policy and services in Boston. We're exploring what it takes to support government community collaboration in an ongoing way. And we're doing this by practicing collaborative governance in a network setting. Collaborative governance is a method of solving complex problems in society with collaborative leadership teams from communities and government. People who work closest to the problems are invited as experts to co-design solutions with those who have resources and influence for those problems. Those of us with lived experience are often asked to lead solutions and co-design processes that help the community and uplift their voices with the support of academia and political offices. This has played out in a number of ways. I suppose one of the most interesting is a project called Getting Connected that we did early on in the network. Everything went online and people without the knowledge or the comfort level to be in online spaces were being left out. A project team emerged out of PCGN and out of that was a guide. Super simple project with a room. Hey, I think I went away. Yeah, you did. For a second. Yeah, I'm back now.  That was identified by the people who were actually experiencing it. And so the outcome was something that was immediately usable. And we've seen it spread across the city in ways that are just really exciting. We are able to be in spaces across disciplines, across titles, across power dynamics. We can realize that we can be motivated in that same interest, in a greater and better Boston. We are a community of Boston residents, government officials, academics, and community organizations across multiple neighborhoods. This is a laboratory where we explore and create the tools and the practices needed to solve Boston's wicked problems through collaborative governance. PCGN. Okay, so I show that project as a way, again, of imagining a different sort of an alternative structure to sort of traditional mechanisms of holding space, right? This is not just the town hall, not just the form to collect the data, but these alternative sort of collaborative governance structures are essentially, potentially replacing some of those. And they have implications that are important for this listening paradigm. Why does that happen? Okay, so let me just get ahead here. So holding space means creating environments of exchange that ensure the necessary conditions for dialogue. And that make room for multiple perspectives and as tolerant of dissent. It implies not only establishing a field of action through trusted links with the involved stakeholders, but also purposely designing the conditions, time, place, mediums through which participants can engage freely. Being intentional about design for democratic exchange means creating spaces that mediate between different kinds of knowledge. What Indigenous scholar Ellie Inns calls ethical space is an intermediary between two knowledge systems, a common space designed specifically for this collaborative encounter. So in this shift towards listening, holding spaces come to mean any physical or digital platform where people share their experiences, not necessarily in response to institutionally framed prompts. So I'm going to talk about civic imaginaries as the third component here. Henry Jenkins defines civic imagination as the capacity to imagine alternatives to current cultural, social, political or economic conditions. And he says one cannot change the world without imagining what a better world might look like. And he argues that the civic imagination requires and is realized through the ability to imagine the process of change, to see oneself as a civic agent capable of making change, to feel solidarity with others whose perspective and experiences are different than one's own, to join a larger collective with shared interests and to bring imaginative dimensions to real world spaces and places. So these imaginaries are always informed by technology. Jasanoff defines socio-technical imagineries as collectively held and performed visions of desirable futures. So how one imagines the future is going to be informed by and generated with technologies in some way. So what we're looking at here is a mural that was part of a public exhibit in Boston that asked people to produce imaginaries through past, present and future technologies. The goal is to spark stories of technological futures that were informed by technological past. So stories of desirable futures have long been told within communities, reflecting community-based knowledge and narrative practices. But these efforts typically emerging long before the start of official planning processes are overwhelmingly disconnected from institutional procedures, providing barriers to much needed integration of local knowledge. And the challenge for practitioners is to figure out how to operationalize these imaginaries into concrete future visions, enabling community stories to effectively reach and support institutional decision makers in ways that are discernible to them. I'm going to jump ahead to sense-making, which is the fourth component. This moves towards listening. The move towards listening requires exploration of how institutions then respond to the data and the stories and demonstrate that they have listened. So specific technologies like decision support tools and dashboards can be utilized to incorporate different viewpoints. They can complement steps and validated processes for planning, such as risk and vulnerability analyses, and aim to inform discussions, show weak points, knowledge gaps and uncertainties, and visualize trends. However, what data is collected, how it is contextualized, who gets to see it, are all decided upon by institutional actors. Sense is being made of vast amounts of data, but what data and whose sense? So more promising, technologies can be utilized for the dialogic function of reconciling multiple points of view sourced from aggregated imaginaries into nuanced suggestions and documented decisions. So while artificial intelligence is often seen as a means for rationalizing knowledge and depriving it of context, there is an opportunity to utilize it to develop and test mechanisms to pull insights from a range of stories, scientific reports and news articles, so that institutions and partnering communities can see the same data or hear the same data. So this I'm going to harken back to real talk for change, which I mentioned earlier. And this proposes a methodology for community members to share stories, which are processed by a software that can retrieve the deeper texture and complexity of the views of the community through qualitative data captured in conversation. These insights are useful for institutions to understand the values of the community engaging and planning processes in a language that institutions can act upon, taking part in effective institutional transformation that bypass communicative disconnects and produce effective collaboration between institutions and the communities they serve. So the role of a technological layer in processing data is an important one. And while in some cases, a little AI may provide trust where traditional institutional actors lack it. And in some cases, technology is an impediment to interaction. And so the way that this is playing out in practice is complex, right? So in some cases, depending on the existing trust in the institution, the the positionality of technology is going to is going to differ. Sometimes it's going to build trust because there is no trust in the human institution. And sometimes it will be met with distrust. And which leads me to my last point, my last component, which is accountability. And so what we see in this trend towards listening is an emphasis on accountability. So this also involves having mechanisms and tools to monitor the details of a plan to hold institutions accountable. So my monitorial citizenship as defined by Schutzen refers to a form of civic engagement in which people collect information about their surroundings or track issues of local or personal interests in order to improve their communities and pursue justice. Where common activities of the monitorial citizen include collecting information, sharing stories and insights, coordinating with networks of other civic actors and pursuing accountability for institutions and elite individuals and their perceived responsibilities. So while these practices are frequently performed outside the scope of institutions, city stakeholders are actually looking to incorporate tools for communities to evaluate and monitor institutional actors. So in city government, this is manifesting in how people imagine dashboards or ways for agencies to represent data so that they can point to and justify decisions they make. So from education departments to food access programs, government actors are motivated to have something to point to to hold themselves accountable, but also to hold communities accountable for their reactions. So in this case, accountability is also a matter of self-protectionism as much as it is about authentic accountability. And so we can look at some tools like this edim and real talk for change and we can see that they are attempting to move the demonstration of data outside of the institutional setting. So we see a trend in government like what we see here at dashboard that looks at, this is a dashboard of 311 reports in the city of Boston that is used as a way of sort of tracking and holding certain departments accountable, but also as a means, as I said before, of holding communities accountable to their reactions. So it does get it does get sort of manifested as an insurance policy for government actors at times. So I'm going to conclude by just by talking about a project called data blocks that we ran back in 2018 and just as an example of something kind of a more in-depth case study example of a smart city engagement process, let's say. So and this is a paper if you're interested in this paper that recently came out in the International Journal of Human Computer Interaction about the beta blocks project. So this was what beta blocks was was trying to do was trying to address this this challenge of communication between public sector actors and technology companies and trying to find a point for community to intersect within that within that relationship. So in a sort of an experiment of experimentation, we sought to design a system that would create friction and the norm of closed background dealings where tech company X sits down with bureaucrat Y to solve the problems defined without consultation with communities. And so what we did was we created these these components of what this process would look like. That included what we called an exploration zone, an advisory group that governed that zone of traveling public exhibit, a specific attention to youth and then a recommendation system. We house these exploration zones in three places within the city that was defined by kind of social economic diversity within the city and geographical diversity. And exploration zones are defined, we define them as four square block areas where there can be experimentation that happens. So technologies can be temporarily installed for community experience and input. We identified based on existing physical and social infrastructure that was in place. That's how we defined what the four blocks were going to look like. We relaxed permitting from the from the city. And then the whole thing was governed by an advisory group. Each of these zones had an advisory group that was assembled. This advisory group met met regularly and determine what was being put in the zone, how it was, how we were looking at the data, what the data was that was coming out of it, and how that data was shared. So some of one example is a series of billboards that we installed throughout the city called super signs. These are solar power billboards and the communities decided where they were going to go and what they were going to show. We put air quality sensors throughout this throughout the zones as well. And this was also designed by the advisory group. And then we also could co-design these signs here that you see that were there to sort of describe what was happening, what this actually was that you see this is something that was placed in Chinatown. And so and then we worked with the community again to sort of figure out where everything was being placed throughout the city. While this was going on, we had a traveling exhibit that we called the Beta Blob that moved about the city and it became a place for interaction and dialogue about the role that technology was playing in the city. And we invited participation and conversation about the role that data plays in people's lives and what they would want to see difference. And so the this big inflatable structure kind of moved around as an attention grabbing function and sort of brought people to it for the purpose of cultivating some kind of listening. Here we see it's placed in a park. Then it culminated in an event at City Hall where we sort of use this as an opportunity to define to a larger city government what was happening in the city with the Beta Blox experiment. And so where I want to end here is so the project concluded with an exhibit at City Hall, as I said, so that government officials could take notice of a novel mechanism for decision making. At the end of the day, we learned that we could build structures to intervene in existing modes of procurement. We could create ways of showcasing scientific and community data in a way that made for more informed decisions with transparent procedure. What we couldn't accomplish in the one year of funding we received from the Knight Foundation was actually institutionalizing the new mechanism with the necessary stakeholders in government. So we have a model that informs three components of deep listening. It held space for diverse stakeholders, supported imaginaries, and provided structures for making sense of them. But incredibly difficult for these kinds of structures to be sustainable in practice. So I've provided a critical and normative examination of the trends in urban government in urban governance. The use of more and larger datasets, predictive analytics for decision support, and machine learning to anticipate public needs are just a few of the ways city governments are exploring machine aided listening. In a context where every institution from big tech to hospitals is reconsidering how it connects to its public, city governments are following the same trajectory. Cities are undergoing a paradigm shift from institutional engagement to institutional listening, which implies a different role for public institutions shaped by changing perceptions of institutions and expectations of responsiveness in all facets of life. Every institution I argue is experimenting with listening tools and protocols right now and very few are getting it right. So the challenge for scholars and designers is to help make sense of this transition and to empower well-intentioned institutional entrepreneurs to take action. Thanks very much. Thank you so much, Professor Gordon for your talk that was very fascinating. So I would like to open up the session for questions now. And just as a reminder again to ask questions, participants on Zoom are encouraged to use the raise your hand button, and then I'll call on you to unmute and you can ask the question directly, or you may also type your question in the chat box and I can read them out loud. Maybe I'll start with the question. So I think I find the potential of digital tools to enable and augment the participation experience and now deep listening is very fascinating. But I will also imagine kind of digital engagement tends to favor a certain segment of the population, but not others. So for example, young people, Millennium Gen Zs might be more interested in testing out new experiences that's virtual or dealing with technology. So could you maybe talk a little bit about the profile of participants from your experience and do you see there being a digital divide? And if so, what might be some ways of bridging the digital divide or is it more about providing complementary perhaps physical means of engaging those who are not part of the digital engagement experience? Yeah, I mean, I think you kind of answered the question and that it what what I'm seeing happen is mostly as mostly an emphasis on hybridity. I mean, there are very few practitioners that I've talked to that are interested in digital only engagement tactics, understanding that that's problematic. You know, COVID actually did wonders for shifting people's levels of comfort with with digital engagement, let's say, the town hall meeting, for example, that in many cases in many cities there were laws against holding them online, because they didn't meet public meeting requirements, are now are now acceptable because of because of COVID, right? So like those those those laws have changed in some places. And so there is there is a greater comfort level, which I think is part of this move towards, you know, part of this move towards deep listening, right, which is the idea that that if the role of the public sector is to listen to what to what is going on in communities and to apply certain technological mechanisms to aid in that listening strategically, that's what we're seeing, right? We're seeing we're seeing a desire to do that. Now how it's playing out in practice, as I said, is totally messy. And I'm not actually, I don't want to say here's the right way to do it. I'm actually more interested in a range of practices that that represent what what is a paradigm shift, right, which is a which is a move for, you know, for many government practitioners even stuck within their institutional arrangements are very willing to say, you know what, government can't can't always set the table anymore. And yet they're stuck with an institutional arrangements that actually enforce that to happen, right. But they but often individual practitioners within government are saying, we need to we want to do we want to do things differently, we want to listen differently, we want to we want to come up with mechanisms that actually bring people in differently. And they're they're pushing up against institutional barriers barriers in order to get that done. And so what that looks like in practice is just a range of things. You know, in an age of COVID, we're seeing many more digital zoom based town hall meetings. We're seeing many more digital tools being being used. We're seeing much more emphasis on data collection of all sorts, and the cultivation of feedback mechanisms to provide provide people insight into how decisions get made. But it's not we will soon see more in person interactions that are led by public sector organizations as soon as that becomes more COVID tolerable. So so that will come back. It's not going away. But I think that there's a there is a insight among most government practitioners, let's just say for the moment in the US at least, that the that having a multimodal approach to engagement is is necessary. And so the digital divide question, I think is on people's mind, but I would I'm hearing more from practitioners now that the that the challenge of access is perhaps more acute when it comes to the physical meeting space being the barrier to participation. And, you know, again, that's like a that's a very post COVID or not post COVID, but let's say during COVID kind of interact response to this trend, right? Like that that would not have happened two years ago. But it is happening now. And I think that's that's also a sort of what's sort of pushing this move away from traditional engagement paradigms. Thank you. So I am a qualitative researcher. And a question that I've been interested in is whether the type of information that people feel comfortable sharing over digital platforms or other virtual platforms might be different than the type of information that they're comfortable sharing in person, right? That they, you know, it might be more comfortable sharing sensitive information when they are not in person, because it feels less kind of intimate with the with the person gathering the information. And I'm curious if you have seen anything like that in terms of the the trust dimension that you've been talking about, where, you know, if people are not necessarily trusting a particular institution, might they be more comfortable sharing sensitive information or information that that differs in other ways from information that they would share in person? Like, how does how does that comfort with information sharing differ? Yeah, absolutely. It's such an important question. And see, how do I how do I start answering this? The answer is yes. The answer is yes. There is a different level of comfort based on the the modality that people are operating within. In some research I did on that community planet platform that I shared with you back in 2013 or so, we studied the how youth were using the platform. And this was what was interesting about that platform is that we would do a planning process that would take three weeks. And it was facilitated via this online tool. And and over over three weeks, we would get a couple thousand responses in this in this thing and any given in any given planning process. And and half of the people who participated in that were youth under 18. In many of the cases we did, I'll talk about I'll talk about implementation we did in Detroit. And so we did this we did this project in Detroit, a couple thousand responses in the platform, half of the people participating were under 18. There was a whole like we arranged a sort of whole school based competition that were existing within within the game. It became very clear within that interviews that we did with with participants that for the youth participating in this platform back in 2013, they felt far more comfortable expressing themselves and even participating online. So that was an early insight to to say that like just simply by nature of it being online, it invited it invited a different kind of participant participant and a comfort level with the with the platform, those youth never would have shown up in person to a town hall meeting so that that's that's a sort of in some ways a no brainer that that provided access in a way that was not possible before. And certainly now we see again in a sort of like in a in a in the context of COVID. We see a much enhanced level of comfort even among older adults with with some online platforms in many cases actually seeing an increased desire to interact that way because it because it's seen as as far more convenient. So like it has definitely moved the needle in in the in the sort of digital non digital context. But I do want to talk about this trust issue and and the trust and that plays out differently based on the based on this democratic context that we're operating in. So for example, in when we look at what's happening say in Buenos Aires in Argentina, we see and and the use of emerging sort of uses of blockchain technology and governance, we see a very different kind of manifestation of tech enabled participation that bypasses the bypasses in this case, the government institutions that that that people do not trust right so when we when we have when we can say that there are government institutions that are that are lacking in trust significantly enough where the the the technology provides a way in whereas there wouldn't have been a way in outside of that technology. So in this blockchain in Argentina is a really interesting example right it provides a way in where you know trust in economic systems and government systems are are actually allowing technology to be a bridge essentially a trust bridge to participation in a way that is not would not have been possible. Whereas if we look at something that like in Philadelphia for example in a project like Zen City, it's a very different response right it's a very different even though trust in government is low. There is still the trust in the trust in technology in many cases is lower depending on who it is that you're talking to so the idea of an algorithm spitting out spitting out conclusions based on social media activity is already getting a negative response in some sectors because of the because of the role the machine mediator the machine trust broker. And so I think it's it's a really interesting question and I think that we have to continually understand technology in relation to democratic context and also in relation to demographics right so all of those things are going to matter so there's not one solution not one answer. This is all part of this kind of experimentation phase that we're in right now as we understand these transitions and governance. I have a question. I was wondering I was very interested in your discussion about accountability and the notions of dashboards that are popping up now. Yeah. And I was wondering if you could talk a little bit about you talked about government accountability and that's pretty clear and obvious but you also mentioned community and other groups accountability and I was wondering if you could talk a little bit about what is their accountability you know how if this is a collaborative process of decision making what does this sort of accountability collaboration model look like? Yeah. Let me take that question in a couple of parts. Let me start with the dashboard the dashboard concept. Yes. There is a heavy use of dashboards within government as you know that like everyone wants a dashboard. We just just did this study with with city employees asking them about their uses of data and almost every single person I interviewed when asked about what their sort of hopes for future uses of data all of them had some version of I want a dashboard that is like real time responsive it's got all the data it's got all the things and then I can and and not only can I see everything that's going on but but the public can see everything that's going on right like I was like some version of that was what everyone wanted this this imaginary of a dashboard that was a complete window into what was happening in the city and so that what's interesting about that is that what I was trying to say about like this government accountability yes but it also is government protectionism because it says like okay we're going to be accountable but then we can point to this thing because we often get blamed for things that we do and we think we have really good reasons for doing it but we don't have any evidence to like show how our decisions get made so like now we have evidence we can point to this thing and we can and and we're we're covered so so that's going on that that is a phenomenon that exists at the same time we saw and again especially you know since covid we've seen a we've seen a a really interesting increase in the way that communities have been using data and collecting data themselves so specifically with things like with like food sharing programs and access to food you know there's all sorts of interesting examples in in cities of of community driven efforts of data collection that provide that provides some demonstration some sort of universal knowing of what kind of food resources are there because the city doesn't have won't share or isn't doesn't have complete data about about where food resources are so so we see community led efforts to do this and what's interesting about that is that there is this attempt within these community led efforts to create a kind of accountability to two people by through the use of data through the collection of data having access to that data showing what's there what exists provides a certain kind of accountability when people say if it's community led if someone says I didn't have access to food there's an there's an ability to point to like yeah it's here you know there and then and then other people can sort of collaborate and and share resources to get food to that last place so that's on the community side and and there are other examples other domains where we see some interesting community led community only efforts of data collection I think what's really interesting and really really challenging is that collaborative like how communities can effectively share data with with institutions with with government institutions and what kind of agreements need to be in place for communities to feel comfortable with that so an example I mentioned in my talk but I'll say it again which is specifically was a community leader who was talking about qualitative data that she generates within her community and all the conversations that they've had and then and then policymakers of various sorts will will reach out to this CBO and say you know I want to know more about the issue so so give me that your data give me your information your community dialogue information and the policymaker will then take that information and you know do something and then and then pat themselves on the back and then say good job to the to the leader of the CBO and then go off and do the next thing academics do the same thing right we always go into we go into communities we can sometimes be extractive in the way that we collect data we publish a thing and then go away and off to the next thing and can and community organizations see that and so the challenge then becomes how do we create data collection and sharing protocols where both both the institutional frame and the community frame have trust in how that data was collected and how that data is governed and this is where the accountability problem becomes incredibly complex and and something that I don't have an answer to exactly I haven't seen a single system that I can point to to say that's how it should work but what I do see is that this element of accountability is something that is more on the minds of government practitioners more on the minds of community leaders and and the access and use of data is pushing that accountability conversation in new directions right so because it's how people people always talked about accountability but the way that accountability is being talked about through data now is different and I think that building those kind of socio-technical systems that allow for that shared accountability is really important and and again not something that I know the answer to but something I know needs to happen in order for this kind of smart urban collaboration to take place interesting thank you great thank you I think we have time for one more question one or two more maybe I've answered all the questions a lot for us to think about for sure no more questions I think maybe we can end it a little bit earlier if we don't have any more questions from the audience all right so on behalf of GSAP and the urban planning program in particular I'd like to thank you again Dr. Gordon for your great presentation today we really appreciate you taking the time to share your work with us it's been really fascinating and thanks also to everyone who was here with us today so please make sure to join us again next week at the same time for our next lecture by Dr. Bernadette Bear-Zerns whose talk will be on ground rules how city officials tweak urban futures through great institutions of land use thank you thank you


The application of intracranial electroencephalogram, EEG, to predict seizures remains challenging. Although channel selection has been utilized in seizure prediction and detection studies, most of them focus on the combination with conventional machine learning methods. Thus, channel selection combined with deep learning methods can be further analyzed in the field of seizure prediction. Given this, in this work, a novel EEG-based deep learning method of one-dimensional convolutional neural networks, 1D-CNN, combined with channel increment strategy was proposed for the effective seizure prediction. First, we used four seconds sliding windows without overlap to segment EEG signals. Then, four seconds EEG segments with an increasing number of channels, channel increment strategy, from one channel to all channels, were sequentially fed into the constructed 1D-CNN model. Next, the patient's specific model was trained for classification. Finally, according to the classification results in different channel cases, the channel case with the best classification rate was selected for each patient. Our method was tested on the Freiburg EEG database, and the system performances were evaluated at two levels, segment and. This article was authored by Xu Shuan Wang, Qi Zhang, Tommy Karkanen, and others. We are article.tv, links in the description below.


It's not like made of noise. I think this thing Make some breakfast with eggs with the emerald. Hmm. Okay, let's see what's in the uh, let's see what we can cook actually I think eggs Eggs is good. Okay, so I don't know what is wearing this kitchen and I don't want to ruin anything But I think MVP for all this stuff. Okay, we're gonna find out where the pots and pans are That's a great question So chat this is now that we make eggs in the S4 fashion. First things first is we need butter or pan They have a really good kitchen Okay, so chat we're gonna make bacon eggs today First things first, I have my hands locked. I wash my hands this morning So we're gonna take some butter Butter is bad. I used to use oil, but the reason why I don't for real is because it splashes. Butter is not splashed So now I go butter, and I go like that Protein chat Protein and butter chat You guys got Tonys? Tonys? Yeah No, who's Tony?  You guys making some bacon? Yeah It's broken again We use this actually, isn't it? This is your first time here and you don't know how this works When we do cooking streams, it's very very simple You have to flip the eggs If we can't effectively, successfully flip the eggs, then you don't do this But I know you guys gotta cook prime, okay? I know you guys are sitting there, it's been sitting there the whole time Okay, forgot that you didn't have it Just click the sub button Check the box Sub for free with prime or whatever it says But don't use it, don't Until I can successfully flip the eggs In somebody else's kitchen And I'm not gonna make a mess Are you doing nothing? Ready? Three, two, one Wow There we go Guys, we did it! And it looks good, and it's not burnt On four I love you a little bit I had this These are like over hard Oh, look it's fine Okay, ready? Three, two, one Like, these are not burnt chat You're gonna say we burnt them These are not burnt, so good I think this was pretty good I like kind of raw I like mine crispy, we need forks You know what I just remembered I just remember that club of people posting That Club of you eating noodles And people dance gaming that you were Smalling noodles full or something I don't even know Okay, so I ate my bacon, it was incredibly delicious And also a little bit burnt Is it bad to eat food that's burned a little bit? Um, I don't know My mom said you can get an answer Yeah Yeah That's what I think I want to do that too Great, now I'm gonna chat Chat, do I make more bacon? Do I make more bacon? Great, okay fine Okay, new plan chat We're going to make a Bacon egg taco tortilla Uh, thing So here's how we're gonna do this chat We're gonna try something new here Okay, so this is what we're gonna do We're gonna put bacon on top of the circle of eggs Over hard So put all the bacon on the side of the egg We're gonna fold the tortilla egg over And have it half So we're gonna try this We're gonna try to We're gonna try to To heat up the tortilla Around the eggs and the bacon I saw camp Rollins do this And he's a cowboy You're trying it It looks worse on camera I swear, it's perfect It smells like it's burning No, it's You know what's sitting here with the heat It actually started curving on its own So I think I'm good See how it's curved? I think I'm good I'm fine I think this looks so good I want to eat that too What's the slaves you ordered? What did you get? Eggs Onions The goat version Is it good? They've already Shot three times Three times? There was one on the way out One there It's raw I don't know What's in your stuff? What's in your stuff? Eggs, bacon I think you should have cooked it The toast was totally worth it A couple minutes Okay Okay, we'll sit down The cameras are rolling But you're welcome to step in I just need to sign a release after Yeah, good to meet you I'm a jackass That's my job title Professional jackass Thanks Oh, sorry I couldn't figure out how to open the door Chad wants to see the goats and I'm going to host Pinks Hi Which one did they pee on? I'm assuming that one Aren't they all purple? Yeah, I actually am colored one Really? Thanks, Mikes The daily dose of ableism That's a lot That's a lot of piss Dose really just eat everything, don't they? I'm going to grab my hat Give it a ladder for content Can goats climb ladders? I think it will pee on me if I hold it Can I hold it? How much did it weigh? Yeah, how do I Alright, Sven You can do it Is he going to poop on my hand? It's okay if he does Do I wrangle him? Just go from the bottom and scoop him up You got this, Sven He's not Jesus and his goats Okay Chad If you give 50 subs It's just happening I can't make the goat poop Just think about it Think of all the pellets coming out of that goat putt Is stomach made of noise? I don't think so I know Maybe I just asked you No, I don't want to ask them Because I know their answer


20 ANIMAL IDIOMS IN ENGLISH, FROM ESPRESSOINGLISH.NET 1. THE ELEPHANT IN THE ROOM The elephant in the room is an idiom for a problem or a controversial issue that is too big to ignore, but that everyone tries to avoid talking about because it is embarrassing or will cause conflict. 2. ONE TRICK PONY A pony is a small horse, which is often used for shows, competitions, and exhibitions. People teach ponies tricks to perform at the shows. If a pony only knows one trick, then it doesn't have a great variety of abilities. So describing someone as a one-trick pony means the person only has one ability or one good quality that he or she is known for and doesn't have any other abilities. 3. THE LION'S SHARE The lion's share of something is the biggest part or biggest portion. 4.  5.  The origin of this idiom is unknown. 6.  7. WEASEL OUT OF SOMETHING Weasels have a reputation for being sneaky. If a person weasels out of some responsibility, it means they abandon their responsibility or commitment in a way that is sneaky or cowardly. 8. THE CAT'S MEOW If you think something is the cat's meow, it means you think it is excellent, wonderful or really great. 9. LET THE CAT OUT OF THE BAG This idiom means to reveal a secret. 10. GO TO THE DOGS If something goes to the dogs, it means it goes bad, deteriorates or becomes poor quality. 11. LET SLEEPING DOGS LIE If you make a suggestion to let sleeping dogs lie, it means not to talk about things in the past that might cause problems if you mention them today. This idiom comes from the fact that a sleeping dog is peaceful, but if you wake it up, it might be angry and bite you. 12. WILD GOOSE CHASE This idiom means going after something you are not likely to get or a pursuit that is a waste of time because it is unlikely to succeed. It comes from the fact that it is very hard to catch a wild goose, so if you try to chase one, you are not likely to get it. 13.  Quitting smoking cold turkey would be deciding one day never to have another cigarette again, differently from slowly decreasing your smoking habit over time and eventually stopping. There are two possible origins for this idiom. One is that when someone is addicted to drugs and suddenly stops using the drugs, their skin becomes cold and gets bumps like a plucked turkey, a turkey without its feathers. The other possible origin is the fact that cold turkey is a dish that is quick and easy to prepare. 14.  That means it hunts small animals for food. Because of this, a hawk needs to have very good vision and watch carefully to find the animals, so watching something like a hawk means watching extremely carefully. 15.  If a fish is outside the water, it would feel very uncomfortable. If you feel like a fish out of water, it means that you are very uncomfortable in a particular situation or environment. 16. FISH OR CUT BATE In the sport of fishing, bait is the food you put on the hook to attract the fish. If you cut bait, it means you abandon the bait and stop trying to catch fish. This idiom means either do something or else get out of the way. You can say it to a person who is indecisive in order to motivate them to take one action or the other action. 17.  This idiom comes from the fact that a herring is a type of strong smelling fish.  18.  19.  Hornets are dangerous when they are angry because they can sting. 20. WOULDN'T HURT A FLY If you say that someone wouldn't hurt a fly, you are describing the person as very peaceful and non-violent. It is a person who is so gentle that they wouldn't hurt anyone, even a small insect like a fly. For more English tips, visit www.espressoenglish.net. If you liked this video, please share.