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Data is powerful, allowing crucial information about the prevailing social and material realities of our world to be conveyed. It can be used to advance very different and sometimes opposing goals. Indeed, data is often leveraged to identify issues, highlight disparities, and advance solutions to pressing social problems and may even be used by social movements to empower organizing efforts and articulate the theretofore underrepresented and unarticulated social problems of their base – this represents a utilization of data ‘from below’.

However, certain actors and institutions routinely use data to propagate violence, exacerbate othering across myriad social classifications, and reify and make anew the preexisting conditions of inequality and resource maldistribution that are pervasive in the world (a hegemonic reinscription through data). Information is a resource, and insofar as that resource exists, it is often used by those with power to reinscribe existing relations of power.

In this panel, we survey a set of domains at the interstice of data and civil society: racialized policing and surveillance (Stop LAPD Spying Coalition), housing and eviction (Anti-Eviction Mapping Project), and belonging metrics (Othering & Belonging Institute). Taking these important problem spaces as a starting point, this panel seeks to generate a productive dialogue where the specific might intersect with the burgeoning general on the topic of data and belonging/othering. We posit that understanding how data can be both used and misused is critical for creating a world of belonging without othering.

Curated by OBI's Equity Metrics team.


Speaker 1:
Hello, and welcome to this special episode of Who Belongs, a podcast from the Othering & Belonging Institute at UC Berkeley. This episode is part of a series of talks and panel discussions recorded during the breakout sessions of our Othering & Belonging conference that took place in Oakland this past April. This session is titled Using Data to Advance Belonging Without Othering. It looks at a set of domains at the intersection of data and civil society, such as racialized policing and surveillance, housing and eviction and belonging metrics. Panelists include Amy Lee, who is an organizing member of the Anti-Eviction Mapping Project, Matyos Kidane, who is a community organizer with the Stop LAPD Spying Coalition and Shadrick Small, who is a researcher at OBI. It's moderated by Stephen Menendian, who is the assistant director at OBI. This panel is curated by OBI's Equity Metrics team. You can find more episodes from this series on our website at belonging.berkeley.edu/whobelongs.

Stephen Menendian:
So what we're exploring here today is the uses and misuses of data. We want to understand how can we use data ethically, not just ethically, but to promote belonging and to avoid harm and othering? And we have a really phenomenal set of perspectives here to interrogate and interact with you on that question. My name is Stephen Menendian. I'm the Assistant director and research Director with the Othering & Belonging Institute, and I want to thank the Equity Metrics Program of OBI for organizing this. That includes the program director, Samir Gambir, Joe Arenholtz, Joshua Cantong, and Yves Liao, who are all in the room. So thank them.

First up, we will have Amy Lee. Amy Lee is an organizing member of the Anti-Eviction Mapping Project, a data visualization critical cryptography and multimedia storytelling collective that documents dispossession and creates tools of resistance for movement building. She also provides policy and research support to write to the city member organizations working to stop displacement and build democratic just and sustainable communities.

Second, we will have Matyos Kidane is a Tigrayan immigrant who has lived in Los Angeles for most of his life. Matyos is a community organizer with the Stop LAPD Spying Coalition, a community-based abolitionist working group, working to dismantle the National Security police state. And last but not least, Shadrick Small is a staff researcher for the Othering & Belonging Institute, a sociologist by training. He joined OBI in 2022, and his work is largely concerned with belonging metrics and other empirical assessments of belonging. Please welcome the panel.

So we're going to do brief presentations from each of them, a panel question for each of them, and then open it up to you. We'll start with Amy.

Amy Lee:
Hi everyone. Thank you so much for the invitation to speak. On behalf of the Anti-Eviction Mapping Project, I just want to use my presentation to introduce you to the work of the Anti-Eviction Mapping Project, which is very much a collective. So I am speaking on behalf of the collective. And I do have a disclaimer here, which is that I'm not a data specialist, but I do believe that everyone should be able to talk about data, which is why I'm here to talk about why data is important in our work.

So the Anti-Eviction Mapping Project is a data visualization, critical cartography and storytelling collective. And we are a very diverse collective. We're fairly interdisciplinary. We're all volunteers. So most of us have our day jobs, which I won't talk about here. And we try to contribute whatever skills we have to the collective. But we all share a commitment to social and racial and housing justice. And so it is a politically-motivated organization, and we do have political commitments. And so that's also... And I will talk a little bit more about this later, is that when we use data, we don't think about data as something that's neutral. We also try to make data accessible to our communities in ways that advance a housing justice movement.

And having said that, we're all individuals, and many of us are housing activists and organizers in other capacities, but a lot of the work we do is in partnership with community organizations. So we're very much plugged into the housing justice movement, and we try to create tools that would be useful to the movement, and not just for the sake of research or finding out information or gathering data. We've been around since 2013, so in the last 10 years, we've created many maps. We are a mapping organization, we've done oral histories, we've done toolkits reports, and now we are creating digital tools for tenants to use, zines, also books. So we've done quite a lot for an organization that's completely volunteer-run. So just a little bit about history. We began, like I said, in 2013, and we started as a group of housing activists in San Francisco, who thought that we could really try to strengthen the housing justice movement in the Bay Area by creating maps of evictions.

And at that time, we were seeing... This was sort of post 2008, so it was a foreclosure crisis. People were getting displaced, losing their homes, speculative finance, financiers, developers were coming into San Francisco, buying up buildings. The tech industry was booming. So there were a lot of very alarming changes and a lot of evictions that were happening. So we wanted to be able to visualize what was going on so that we could understand some of these questions a little bit better. So who's getting evicted? Where are they getting evicted? Who are the evictors, right? And also, where are people going after they are evicted? And the question of how are people getting evicted? So we mapped eviction trends, and notably, we mapped a lot of what's known as Ellis Act evictions. And so this is basically building wide evictions in San Francisco where developers would take the building, empty out the buildings, and then convert them to condos and sell them off. So we lost a lot of housing through Ellis Act.

So a lot of what we did in the early days was mapping, doing a lot of this, what we call counter mapping, but it's essentially power mapping. So what we're trying to do is also trying to understand the distribution of power across all of these networks, because landlords don't work in isolation. They're also connected to investors. And more and more, we're trying to tackle corporate landlords. So we don't even have a face to the landlord anymore, it's just a corporate network. So we really wanted to be able to visualize that. And some of the earlier maps that we did were around the tech... I don't know if any of you are... Those of you from the Bay Area might've remembered this, but all of the tech companies had these buses that would bus people to Silicon Valley. And what we've noticed was that there were really high rates of eviction around these tech stops.

So it was clear that these tech companies were gentrifying these neighborhoods. And so what we really wanted to do was to visualize this problem so that people have something to organize around. And what was important to us when we're creating maps is that we are not just putting data on a map, we're actually trying to create the context, trying to understand that mapping is not... We're not just mapping in evictions, we're also mapping poverty. We're also mapping displacement. So the mapping is intersectional. And what we've tried to do is to gather different data sets that we could map onto each other so that we could better understand the networks and the processes that are creating displacement in the Bay Area.

But having said that, we did begin as a mapping and a data collective. We've done a lot of oral histories as well. And one of the reasons for that is data alone cannot tell the story of displacement or gentrification. People who are evicted, the process of an eviction is a very traumatic one for many people. And it's not just traumatic for people, it's also traumatic for communities. And so it's really important for us to be able to capture those lost stories, and to be able to understand the neighborhoods that we've lost, to understand some of those longer and deeper histories that come from people who've lived there and then were then displaced. And so it's really important for us to preserve that history, which is why oral histories have always been a really important part about collective. So understanding the life history of that community and neighborhood, and then juxtaposing it and understanding sort of these larger systemic structural changes.

Matyos Kidane:
Greetings, friends. My name is Matyos Kidane. I use he/him pronouns. I am a black man in my early 30s. I have a large black Afro and a beard, and I'm a community organizer with the Stop LAPD Spying Coalition. And yeah, I'm also just going to present a little bit of introductory information about the work that we do in LA. Very thrilled to be here in the bay. There's a lot of intersection, obviously. I'm going to touch on some of that and look forward to learning from y'all and learning with y'all. The Stop LAPD Spying Coalition is an abolitionist org that was started in 2011, and we're housed in Los Angeles Community Action Network in Skid Row. We're working to dismantle the National Security Police State, as we said at the very top. And the way we do that is through collective community-led research. Research that then creates popular education materials that is then disseminated to community members.

And then we mobilize. This is the most important part, because we are organizers. We don't just stop at the research. We mobilize community members to decision makers, and that's been pretty successful. I'll talk about some of the data-driven policing programs we've dismantled in the past. But we have a few guiding principles. When I say we're abolitionists, we're very rigid about our abolition, right? We're very kind of earnest and unwavering when it comes to abolition. Oftentimes, people tout reform. They tout reform toward abolition. We don't go for reform. We actually organize against reform a lot of the time. And that's kind of due to a couple reasons. But one guiding principle I want to bring up right now that informs that is when we think about surveillance, it goes beyond technology. When we think about data gathering, it goes beyond people being data-fied as inputs for social media purposes for commodification.

We understand surveillance to be the watching of communities, right, with the intent to cause harm. So we often think about lantern laws in eighteenth-century, New York that mandated black and indigenous enslaved bodies to carry lanterns to self-identify in the night. That to us is a predecessor to ankle monitors. We also think about how... And I've got to bring this up here by Berkeley, but how former LAPD police chief August Vollmer was also the founding police chief for Berkeley implemented crime mapping, which is touted as kind of this new innovation, but this was done in the past. And that's actually a tactic he picked up in the Philippines. And so these are tactics that have been in play since this settler colonial project began its work. And they're not replicating these programs. It's not a replication, it's a continuation. And so, again, not a moment in time, but a continuation of history.

So any notion that this mission to control, to banish, to incarcerate, to kill populations can be fixed is kind of a moot point for us because that is the intention. The intention is to do harm. Another one of our principles is to highlight and uplift the creation of another. The other. And so even in thinking about how there's often a narrative attached to justifying these violent surveillance programs or violent killings of police, there's often the black face, the indigenous face. More recently, it's the fear of Islamic terrorism. That's one of the big programs or one of programs we kind of talk about, the Suspicious Activity Reporting program was propped up post-Nine-Eleven based on these narratives. And so just identifying how that is always kind of a tactic that's in place. And another kind of principle I want to bring up is even in naming how long this has been going on and how ubiquitous it is, we're building power, not paranoia.

So even in identifying the creation of another, we recognize that we're in a room full of others who are working to dismantle these systems. And so, that's kind of how we operate. We're based in Skid Row, and that's an intentional decision. When we think about communities who are most impacted by violence, by police violence, by state violence broadly, we have to uplift unhoused black communities in LA. And in LA, skid Row is unfortunately one of the last remaining majority black communities in the city, and it's telling that they've been, well, we've been kind of confined to a community that's been blighted and still have maintained our existence and still have created a restorative environment in that place. And so, when we talk about surveillance and we fight back against surveillance, again, rooted in that abolition, it's no longer in this whole notion of privacy rights, because if you're a Skid Row community member living in a tent, you don't have a right to privacy.

Your entire existence is out on display. And we also don't frame it in some kind of constitutional right to privacy because we also know that most of our communities have never had rights based on the Constitution. For a lot of our communities, the Constitution has been a framework to sanitize and justify oppression. And so, that's kind of where we come from. That's how we think about it. And even in thinking about some of the reforms that we fought against historically, as I said at the beginning, we've successfully dismantled the first generation of data-driven policing programs in Los Angeles. Data-driven policing was a reform. It was intended to be the fix. The introduction of data, the gathering of data, this new gathering of data, the usage of algorithms in order to take the racial bias out of policing. It was something touted by academics. It was something touted by consultants.

Folks made a lot of money with this fix. And Los Angeles pioneered that under Chief Bill Bratton, who was famous for his advocacy for broken windows policing, which we also know to be inherently racist and discredited at this point. But even in thinking about those first generation of data-driven policing programs, there were two programs in Los Angeles. The first was called LASER, Los Angeles, Strategic Extraction and Restoration. And this was a person-based and location-based policing program. The person-based component created lists that were in name generated by algorithms that use a points-based system to target individuals in the community. And so if you had a field interview card filled out on you, that's a point. If you got caught with a gun charge, that's another point. We found out that in fact, there were people who were on these lists who were targeted by police, black community members who had zero points. Never had interactions with the police apart from an FI card, or apart from being targeted in any other reason.

The other was called PredPol. And this was created by a UCLA professor named Jeffrey Brantingham, who in pitching it, talked about how you can predict crime because criminals... The same kind of thinking that a hunter-gatherer used to choose a gazelle versus zebra is the same kind of thinking a criminal uses to pick a Honda over Lexus. This is language. A current UCLA professor used to justify a predictive policing program in South central la. And sure enough, there were racist outcomes. And I don't just mean incarceration, people died as a result of these programs. And they were dismantled, and all LAPD had to say when this was brought to their attention when it was finally successfully dismantled is; this was an experiment. So even in thinking about what communities warrant that level of experimentation.

So yeah, that work has kind of been done through popular education development that I can talk a little bit more about later on. But we created two reports on that, the first being called Before the Bullet Hits the Body. The second is called Automating Banishment, which also included a map that mapped out these kill zones. But yeah, that's kind of the work that we do. And I'll pass it on because I'm out of time.

Shadrick Small:
Hello, everyone. So as Stephen mentioned, I work at the Othering & Belonging Institute. I've done a fair amount of research around belonging metrics. And so I want to talk about where I see this work happening. And then what I think are sort of the general sort of trends about this work. And this is a loose typology, but you see three sort of main sectors where people are explicitly trying to measure belonging. You see it in academic spaces, you see it in nonprofit NGO spaces, and then you see it in for-profit spaces. So people who do DEI consulting are doing a lot of this work for different companies, different organizations.

A lot of this measurement is highly subjective. What I mean by that is that it's based around questions, gauging people's attitudes. So primarily these are Likert scale items. And so for people who don't know, you'll get a prompt with a predicate statement. So I feel included in activities at insert setting. And you're asked to agree or disagree or strongly agree, strongly disagree, depending on who created the survey, there might be an option for neither agree nor disagree. And then that's used to create a score for what kind of belonging you feel. And so, usually there's two main assumptions with these scales, and it's across the board. You'll see these kind of items in the organizational for-profit spaces, nonprofit organizations, and you'll see it also in academic that these kind of items are sort of the core of belonging measurements that usually proliferate. The first assumption is that you can easily quantify someone's agreement or disagreement.

And so what you'll see is a one will be strongly disagree all the way up to a five, which is strongly agree. And then also that the items have equal weight. And so then what you see is either like a summed score for however many items are in this questionnaire or this survey, or you'll get an average. And I'll speak for me, my problem with that is that A, it assumes that you can easily quantify people's attitudes about things. What does it mean to have a five versus a four? And then also it sort of takes itself for granted. So once we quantify something there's a cultural value being able to quantify things that automatically, people are more inclined to give it credence. And so then it incentivizes people to sort of do this automatic quantification regardless of whether or not it makes sense when you break it down.

And so then given an average, and then you compare people based on different groups if you have demographic information. And so you'll see the average for white folks in this workplace is like a 4.2. And then for people who are black, it's like a 3.6. What does that mean? And you'll use statistical techniques that are used to distinguish between means for groups, and those will give you an answer. Those will tell you, okay, we can expect that this is a rare occurrence based on chance. So there probably is a real difference between how black folks at this organization feel versus white folks. But again, it doesn't tell you really what it means to have a difference of a point in terms of people's attitudes.

But these are the assumptions that are built into most of these instruments. So that's my main spiel, is that we don't really take measurements as seriously as we need to. The second thing, again, these are all subjective measures, is that structure is not taken very seriously. So social structure. And on the one hand, you can't discard the subjective part. I remember talking to Cecily Sorosky, who is our comms director about this. I had the idea of, oh, we'll take the structural stuff and we'll use it to weight the subjective responses.

And what she said, which made a lot of sense, is that even someone who is objectively privileged by a social structure, even if they feel like they don't belong, even everything around them, if they stop to look, tells them that they do in fact belong, that they have more power relative to other people, if they don't feel that sense of belonging, that is a real problem for the group as a whole. So someone who is well-to-do white, cisgender, if they don't feel belonging, and there's enough people who feel that way and they find common cause, that creates a material problem for all of us as we try to live together as one community.

And so then you can't necessarily just sort of wait subjective responses. So you do need to take those seriously. But most of these instruments don't really take structure seriously. And so there's an emerging literature on the academic side, and the Inclusive Index does a lot of this sort of work. So again, Equity Metrics team, shout out, is taking different areas, so states, countries, and looking materially at what sort of things affect people's ability to participate as full citizens.

Stephen Menendian:
Thank you, Shad. Thank you to the rest of the panel. This has been really interesting so far. And we have interesting contrasting perspectives. So Amy sort of described how to use data to map evictions to advocate. Matyos described efforts really under a clear abolitionist lens to move away from the use of data by particular government actors. We are now in this era of big data and AI, and it can be terrifying, particularly this race towards AI, which is going to concentrate immense, not just data but power, incredible wealth and power, and to whoever ends up winning this so-called AI race and how it's used for all sorts of mechanisms of influence, power control, and so forth.

The question I would like to first ask as a general question, and everyone can answer it or a few of you can answer however you want to handle it, is how in this context, can we appropriately use data to advance equity and avoid the harms that can be entailed by the use of data and othering? And are there general principles that we can glean from your work? And how do we do that in really what is a data-saturated landscape where it's almost impossible to avoid data? So you've done immense work highlighting to say, what do we do, generally speaking? Anyone can start.

Amy Lee:
Okay, all right. It's me again, Amy Lee from the Anti-Eviction Mapping project. So for us, we kind of have a very clear target, which is that we are interested in what landlords and developers are doing. And what landlords and developers are very good at doing is to hide from surveillance. So we don't actually... That they are doing all the stuff behind the scenes and we're sort the last to know when the shit hits the fan, so to speak. And so a lot of what we do is a kind of counter-surveillance or surveillance where we are sort of turning the gaze back onto the landlord and the developer and trying to see what it is that they're doing. And in terms of the misuses of data, obviously landlords and developers are sort of the biggest misusers of data. And generally what we've seen is that they increasingly are using a lot of sophisticated technology, not just data, but cameras, tenant screening software, electronic locks, and these are things that they do to really intimidate tenants, but also that they use these incorrect information.

They gather information from tenants, much like the sort of algorithms used by the LAPD to blacklist tenants. And so you could have a similar last name to someone who has an eviction record, and then somehow you ended up on this eviction blacklist, which is privately sold. This is not public information. And so that obviously has an impact on your ability to find housing. And we have no control over that data. That data is sort of sold between corporations.

And also data is also used to perpetuate false narratives. One of the... Concord, by the way, just one. Rent control. But for a long time, for a while, there was this myth that was being perpetuated that if rent control will harm mom and pop landlords in Concord, and most landlords in Concord are mom and pop landlords. But after we did some digging around, it actually is not true. Actually, something like... I have the data here, it's 66% of multifamily units are owned by the top 10% of landlords in Concord, and most of the units are actually owned by corporate landlords. So it's really important to really call out bad data, especially when it's used to perpetuate these narratives that are causing harm to our communities.

So in terms of some of the principles or guiding principles, I guess, about how we as a collective use data, we understand that data is not just a statistic that's from a particular location in a particular time, that actually what's happening today has to do with a long history of the misuse of data. And so when we're looking at gentrification, gentrification is not just gentrification, it also has to do with redlining. It also has to do with the foreclosure crisis. It also has to do with mass incarceration. And so how do we understand the ways that data has been misused in the past to perpetuate those kinds of displacements? And then how does it sort of integrate or how do we understand what's happening today within that context?

So the data is also historical, and I think we often think about data as something that is, I don't know, dehistoricized, but it is for us very much needs to be historicized. Another principle is, and I've mentioned this before, is data is not neutral. So we've looked at a lot of, for one of the reports that we did on Upzoning, in Berkeley, which I'll talk about later. There's a lot of research out there that talks about how upzoning is one way that we can create more housing. And somehow by creating more housing, magically our housing crisis will be solved. And a lot of the data that's used is about creating these models of how to build more housing, how to streamline the process.

But it doesn't actually look at the possible impacts of what would happen if we were to do this, right? Follow this model. And so data is, in many ways, I think it's a misuse, but also in the way that we could use it is that it is speculative. And one of the panelists have mentioned that already, that the data is not perfect. We can't take data as truth. And even when we are trying to map out these corporate networks, we can say with a certain degree of certainty that these entities are networked together, but we cannot for certain say that this is the truth. And so, there is a lot of skepticism when we approach data that we are talking about data not as a given fact, but something that should be debated.

And then lastly, for us, at the anti-eviction mapping project, what's really important is is the data useful to the housing movement? And one example that I can think of is right now, a lot of people in the housing movement are saying that corporate landlords are a big culprit, and we really have to go after Wall Street, and we can't just focus on single buildings. But when you go to the communities, people are organizing in single buildings and they're not prepared or thinking that they're going to go after Wall Street. So what do we do with this data, with this research that's clearly telling us, okay, Wall Street's the problem? How do we use that data in this very sort of day-to-day organizing? I need to make sure this family doesn't get evicted. So I think I'll stop there and hand it over.

Matyos Kidane:
Thank you, Amy. Yeah, this is Matyos Kidane speaking. Stop LAPD Spying. Just a quick clarification. At the coalition, when we talk about the intent to do harm, we don't mean by particular facets of the government, we mean by the government, right? Again, because every agency, foundationally, it was designed with the intention of controlling a certain subset with the intention of serving a specific largely white community, but with the intention of controlling specific communities. And so just understanding that lineage. So we really do see as the usage of data by the state as a whole to be inherently harmful. And let's be clear, let's just name it right now, this is a government at the moment, sanitizing and funding a genocide in Gaza. So just even in that context, that's emblematic of the sentiment and the intention behind this government. And that extends beyond the immediate government, that extends beyond the immediate state to people, to entities that benefit from a relationship with the state.

So that extends to non-profits even, who benefit gain legitimacy grift, academics who grift, that does exist from having a relationship with these institutions. And so we butt heads in the past with the ACLU, especially around their community, community policing, oversight models. And thankfully they've listened. They have, they've walked that back. They've said, "Okay, this is actually what the community wants, so we'll retract our demand and our claim that this is a solution." But the state also recognizes that these are people the state can deputize. And LAPD as a case study in that loves reform. There's a council member named Marquis Harris-Dawson in Los Angeles, who always touts the LAPD as the most reformed police department in the country. They're also still the most murderous, and that should tell you about the function of reform. They're also the most well -resourced, right? And so post 2020, there were a couple of reports that came out that highlighted anti-blackness, racism within LAPD.

And the community was like, "Yeah, we've been saying... We know this." And one of the demands the LAPD put out afterwards, they put out a demand. Again, they love reform. They're like, "This is great. We know just the solution for our racism." And that that was an oversight model for surveillance. That's what they said. They said, "We need a surveillance oversight model." And automatically we were like, "No, that's not what you need. This is just a means of sanitizing your surveillance acquisition." Again, getting more resources. And we got a slew of organizations to chime in and say the same thing, the ACLU community, Coalition, which is mayor Bass's org, which we don't even get down with, but they even were like, "Oh, this is the solution." And funny enough, the LAPD were like, "Okay, no, we can manufacture consent in this situation." And so they actually hit up an Oakland consultant named Brian Hofer to say, "You will be the community representative in this matter."

We got these communications through a public records request. I'm happy to share them after the meeting if you'd like me to. And they brought Brian Hofer on to be the community representative, someone not from Los Angeles, to root for a platform that was the antithesis of what the community was asking for. And what happened after that? The LAPD passed... They called it special order 11, this auditing kind of mechanism. And the very first thing they acquired, they were already in communication with a company called Boston Dynamics at this point was a robot dog. That was the solution to the George Floyd uprising, was the acquisition of a military surveillance robot dog. But I think the root of this question ultimately is there are people working within the government. There are people within academia who want to do good things, people within the nonprofit sector who want to do good things.

And we have a great zine, folks should check out called Know Your Fights. And it talks about how we use the California Public Records Act to do our research, to lead a lot of our research. And a lot of folks would say, "Whoa, that's kind of reforming... you're using an act that's supposed to build up transparency." But at the early part of that zine, we talk about Ruth Wilson Gilmore's kind of analysis on Audre Lorde's quote: you can't tear down the master's house with the master's tools. And just talking about how the apostrophe is doing a lot, lot of work there, how the notion of agency comes into play. If you're using the master's tool with the intention of subverting the state while being grounded in the fact that this is an institution meant to do harm, then yeah, you're doing the work of liberation. And that's how we see it.

And so even when we think about the usage of data, even when we think about uplifting the community first or gathering data, we believe, and this is kind of our take on it, if you're truly grounded in community, if you recognize that you have a commitment, we should all have a commitment in the abolition of an institution that means to do harm, then you're probably using data, but also check in with your community. And so that's kind of the short form answer to that.

Shadrick Small:
We got one.

I think there are sort of two things I want to highlight, and then there's a third thing, but I forgot what it was. And so the first thing is openness and accessibility. I think that's really important. So being explicit about how your data was gathered, what sort of methods you're using to analyze it, what sort of numbers you're crunching, how the calculations are happening. I think that's really important on the one hand, so that it's not just the preserve of people who have access to things by virtue of having more education or working for a university, which gives you a path through paywall. Or, also the other thing is that... So things are open to critique. So it's not just, well, I figured this thing works, so you should just believe me by showing you how I did it. So either you can be like, yeah, looks great. Or you missed a thing. Here's what it is. And that science is a collective endeavor, and that we only get better by sharing our tools and our methods with each other.

The second thing is plurality of methods. I like numbers, numbers are cool, but numbers can't capture everything as Amy pointed out. So being able to not just count things and quantify things, but gather a range of data about how people experience, for instance, belonging. A survey will give you breadth over a wide group of people, and that's the advantage of it. But the unit of analysis is the individual, and we know that belonging is a collective phenomenon. And then similarly, you don't get the depth. And you don't get behavior from a survey. So you'd want to do ethnography. You'd want to do in-depth interviews if you really wanted to sort of understand how people experience belonging in a holistic sense.

Stephen Menendian:
Please join me in thanking this panel.

Speaker 1:
And that concludes this episode of our special series of Who Belongs, a podcast from the Othering & Belonging Institute. For more episodes from this series featuring discussions from our Othering & Belonging Conference in April, visit our website at belonging.berkeley.edu/whobelongs. Thank you for listening.