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. Anchored in Participatory Action Research (PAR) and Community-Based Participatory Research (CBPR), we adopt a reflexive, co-produced approach that foregrounds positionality, reciprocity, and shared decision-making. This approach transformed the researcher-community relationships, leveled hierarchies, and addressed the gaps in familiarity among researchers and other actors. By centering everyday experiences of heat, flooding, and resource scarcity, the collaboration revealed how local knowledge and trust networks shape risk perception and adaptive behavior. The case demonstrates how mutual aid organizations can serve as both community resilience infrastructure and methodological partners in producing usable, justice-oriented climate knowledge. We argue that embedding research within reciprocal, care-centered relationships enhances the legitimacy, ethics, and transformative potential of climate risk management, particularly in urban contexts marked by systemic inequity.
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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".