Putting “Participatory” into Participatory Forms of Action Research
Bibliographic record
Abstract
Although there has been a rise in calls for participatory forms of research, there is little literature on the challenges of involving research participants in all phases of the research process. Actively involving research participants requires new strategies, new researcher and research-participant roles, and consideration of a number of ethical dilemmas. We analyzed the strategies employed and challenges encountered based on our experiences conducting feminist participatory action research with a marginalized population and a variety of community partners over 3 years. Five phases of the research process were considered including developing the research questions, building trust, collecting data, analyzing data, and communicating the results for action. Our goals were to demonstrate the relevance of a participatory approach to sport management research, while at the same time acknowledging some of the realities of engaging 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 | 0.307 | 0.234 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.014 | 0.094 |
| Scholarly communication | 0.024 | 0.037 |
| Open science | 0.005 | 0.037 |
| Research integrity | 0.008 | 0.013 |
| 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".