Collaboration for Improving Social Work Practice: The Promise of Feminist Participatory Action Research
Bibliographic record
Abstract
Feminist research and participatory action research (PAR) share the belief that research should directly serve social justice aims and work to alleviate suffering of marginalized and oppressed people. This article presents the results of a unique feminist PAR (FPAR) approach to designing and implementing an evaluation of an intervention with women who have used violence. The site of our analysis is the steering committee that oversaw this work and the extent to which members adhered to FPAR principles. Over the two decades since feminist critiques of PAR began to emerge, new discourses of collaboration have appeared. As researchers, we must be alert to FPAR discourses that mask ongoing hierarchies. Our findings suggest that, while reflexivity and genuine commitment to collaboration are fundamental to enacting FPAR principles, social workers nevertheless face real challenges confronting structural barriers that impede anti-oppression goals. This study highlights the challenges of adhering faithfully to feminist participatory principles in real-life settings and the need for future research to examine the effectiveness of FPAR processes in achieving authentic collaboration among committee members who are chosen to represent disparate perspectives and are backed by vastly different levels of social and institutional power.
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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 | 0.265 | 0.141 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.018 | 0.077 |
| Scholarly communication | 0.029 | 0.036 |
| Open science | 0.006 | 0.039 |
| Research integrity | 0.009 | 0.012 |
| Insufficient payload (model declined to judge) | 0.007 | 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".