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Hugging Face is the definitive hub for individuals and organizations coalescing around the shared goal of “democratizing” AI. While open AI draws on the ideological values of open source software (OSS), the artifacts and modes of collaboration remain fundamentally different. Nascent research on the platform has shown that a fraction of repositories account for most interactions, ambiguous licensing and governance norms prevail, and corporate actors such as Meta, Qwen, and OpenAI dominate discussions. However, the nature of model-based communities, their collaborative capacities, and the effects of these conditions on governance remain underexplored. This work empirically investigates whether models—the primary artifact in open AI ecosystems—can serve as a viable foundation for building communities and enacting governance mechanisms within the ecosystem. First, we use interaction and participation data on Hugging Face to trace collaboration and discussions surrounding models. Second, we analyze governance variations across models with regular and growing community interactions over time. We describe three phenomena: model obsolescence, nomadic communities, and persistent communities. Our findings demonstrate that the absence of robust communities hinder governance in artifact-driven ecosystems, ultimately questioning whether traditional principles of openness foundational to OS software can be effectively translated to open AI. 

This article explores and characterizes the pandemic urbanism of NYC during the first year of COVID-19. It analyzes changes to the sidewalk and the urban lifestyle using a novel method of remote ethnography: the integrated use of Zoom video conferencing and GPS smartphone tracking to interview participants as they walked and filmed the city. The dataset, composed of transcripts, videos, and routes, was analyzed to reveal recurring themes and visualized through individual Scrollytelling maps combined into one Supermap. The findings are broken down into: 1) changes to the sidewalk, including fewer people, more outdoor sports activity, signs of social distancing, signs of closure, more bikes, and construction; and 2) lifestyle changes, including longings for the urban lifestyle, new-formed solidarity, a renewed appreciation for the local neighborhood, an undercurrent of “moving out” of the city or “moving up” to a better neighborhood, and a difference between Manhattan and the boroughs. 

The COVID-19 pandemic, travel restrictions, and social distancing measures have made it difficult to observe, monitor, or manage urban life. To capture the experience of being in New York City during the first year of the COVID-19 pandemic, we used a novel method of remote ethnography to interview people who were walking the city. We developed the Walkie-Talkie Map to collect and present these interviews, enabling website visitors to see what the subject saw as they walked the route of their choice. Visitors can interactively scroll through the interview and have access to additional visualizations and imagery that contextualize the main narrative. Visitors are thus able to vicariously experience what it was like to be in New York City at the outset of the COVID-19 epidemic. This work provides a case study on how to perform observational research when geographic and bodily distance has become the norm. We discuss the advantages and limitations of our method and conclude with its contributions to the study of cities and for others looking to conduct remote observational research in different fields of knowledge. The Walkie-Talkie maps can be found on this url: https://www.socialdistancing.tech.cornell.edu/what-is-a-walike-talkie 

The effectiveness of social distancing as a disease-slowing measure is dependent on the degree of compliance that individuals demonstrate to such orders. In this ongoing research, we study outdoor pedestrian activity in New York City, specifically using (a) video streams gathered from public traffic cameras (b) dashcam footage from vehicles driving through the city, and (c) mobile phone geo-location data volunteered by local citizens. This project seeks to form a multi-scale map of urban mobility and space occupancy under social distancing policy. The data collected will enable researchers to infer the activities, contexts, origins, and destinations of the people in public spaces. This information can reveal where and, in turn, why stay-at-home orders are and are not being followed. As a work in progress, it is yet too early for detailed findings on this project. However, we report here on several unanticipated factors that have already influenced the course of the project, among them: the death of George Floyd and subsequent protests, data collection challenges, changes in the weather, and the unexpected nature of the progression of COVID-19.