Finding the ‘action’ in feminist participatory action research
Bibliographic record
Abstract
Although feminist researchers have increasingly called for participatory and action-oriented research, there have been few analyses of the diverse actions that can occur. We theorized the actions considered and implemented in a feminist participatory action research project (FPAR). For three years we collaborated intensively with a group of diverse women on low income who were involved in a FPAR project designed to reduce social isolation and other self-identified health problems. Our data set included tape recordings of 32 one-on-one interviews, 15 research meetings, and extensive fieldnotes. Our findings indicated that actions occurred on both individual and collective levels; some had been enacted prior to the project and were shared to promote ongoing or new actions, while others arose as a consequence of the women’s involvement in the project. Additionally, some actions were implemented and actualized while others, though discussed at length, remained hopes for the future. While the research participants reported the benefits of being involved in such projects, they also spoke of the potential risks. Our findings revealed the complexities of taking action in FPAR and highlight important considerations for others wishing to engage in this type of 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 | 0.260 | 0.163 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.020 | 0.110 |
| Scholarly communication | 0.023 | 0.030 |
| Open science | 0.005 | 0.021 |
| Research integrity | 0.011 | 0.010 |
| 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; 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".