From transactional to transformative: evolving research practices through mutual aid collaboration
Bibliographic record
Abstract
While equity in climate adaptation is increasingly recognized, university-based research can inadvertently reinforce inequities. This paper examines a partnership between Homies Helping Homies, a South Philadelphia mutual aid organization, and university researchers to document climate impacts on low-income and marginalized communities. Inequities often arise when research fails to engage communities, overlooks relevant concerns, lacks trust, or misinterprets responses due to insufficient cultural understanding. Mutual aid organizations, inherently community-based, foster resilience and solidarity, addressing unmet needs while building collective trust. the university-based researchers contributed to the reciprocal processes of care and resource sharing that characterize mutual aid by packing food and assisting at events. This approach transformed the researcher-community relationships, leveled hierarchies, and addressed the gaps in familiarity among researchers and other actors. Reframing the effort as a research collective enhanced the researchers' ability to gather insights, resulted in better-documented community perspectives, and facilitated the elicitation of immediate community needs, such as food security, as key components of the research design. The case presented in this paper can provide insight for other researchers attempting a community engaged, participatory, and co-produced research.
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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 | 0.261 | 0.194 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.016 | 0.058 |
| Scholarly communication | 0.026 | 0.026 |
| Open science | 0.006 | 0.053 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.005 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".