Dilemmas in Mutual Aid: Lessons for Crisis Informatics from an Emergent Community Response to the Pandemic
Bibliographic record
Abstract
In response to the COVID-19 pandemic, networks of community organizers and activists mobilized to support their neighbors as part of mutual aid groups across the United States. Emergent community response is a common phenomenon during crisis, but mutual aid in the pandemic took on a distinct character, drawing on traditions of political and community organizing. Our research into these activities suggests that mutual aid organizing in relation to disaster is growing practice but remains evolving and contested. Drawing on interviews with organizers of mutual aid groups in New York, we identify a series of four dilemmas that mutual aid organizers encountered in their work, with impacts on their organizational strategy and technology choices. We then raise three implications for crisis informatics to support community response to disaster: taking a long view of crises, centering questions of equity, and adopting a transformative vision of emergency response.
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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 | 0.012 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.012 | 0.035 |
| Scholarly communication | 0.017 | 0.023 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".