Cultivating the power of partnerships in feminist participatory action research in women’s health
Bibliographic record
Abstract
Feminist participatory action research integrates feminist theories and participatory action research methods, often with the explicit intention of building community-academic partnerships to create new forms of knowledge to inform women's health. Despite the current pro-partnership agenda in health research and policy settings, a lack of attention has been paid to how to cultivate effective partnerships given limited resources, competing agendas, and inherent power differences. Based on our 10+ years individually and collectively conducting women's health and feminist participatory action research, we suggest that it is imperative to intentionally develop power-with strategies in order to avoid replicating the power imbalances that such projects seek to redress. By drawing on examples from three of our recent feminist participatory action projects we reflect on some of the tensions and complexities of attempting to cultivate power-with research partnerships. We then offer skills and resources needed by academic researchers to effectively harness the collective resources, agendas, and knowledge that each partner brings to the table. We suggest that investing in the process of cultivating power-with research partnerships ultimately improves our collective ability to understand and address women's health issues.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.052 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.003 | 0.012 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".