Data activism and social media in the case of racialized and gendered deaths and disappearances
Bibliographic record
Abstract
The use of social media as a medium for challenging mainstream narratives around gendered and racialized violence has been well documented. In this study, we examine the social media practices of data activists based in the United States and Canada who track racialized and gendered deaths and disappearances in Indigenous, Black, trans, and regional communities. A rising form of data activism, these groups painstakingly document cases of fatal violence in spreadsheets and databases. They also use social media to publicize specific cases, disseminate their mission, and connect with their communities. Drawing from a case study of 600 posts from 12 organizations in the US and Canada as well as interviews with data activists, this paper describes how activists use social media to challenge mainstream media portrayals of violence against racialized communities. In particular, these grassroots data activists seek to honor victims and build networks of solidarity through collaboration both online and offline. This work comes with a cost, which is the significant emotional burden required to craft and publish stories about gendered and racialized violence, as well as navigate responses from users on the platforms.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.024 | 0.028 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".