From transactional to transformative: evolving research practices through mutual aid collaboration
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 it