Learning mutual aid: food justice public pedagogy and community fridge organizing online
Bibliographic record
Abstract
This essay analyzes how participants within the community fridge movement use social media to facilitate informal learning and organize improvisational food justice through mutual aid. I offer a qualitative analysis of 20 community fridge Instagram accounts from the US and Canada over three years, with attention to how they disrupt dominant food charity discourses – scarcity, saviorism, and surveillance – and disclose tensions with sustaining their work such as uneven labor burdens and the limits of online activism alone. As such, these accounts offer entry points into critical reflexivity about neoliberal stigma and learning alternative ways to practice radical care amid compounding crises and conditions of food apartheid. Bringing together research on mutual aid organizing, food justice communication, and public pedagogies online, I offer theoretical and applied insights into the role of mediated public pedagogy in food justice activism and the contingent everyday labor of sustaining decentralized mutual aid.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".