Maintaining the Authenticity of Co-Researcher Voice Using FPAR Principles
Bibliographic record
Abstract
This descriptive paper addresses the issue of co-researcher voice suppression, among others, through disclosing my process of presenting the data of a participatory community-based research project at an academic conference. The project in discussion investigated the perceptions of women, who live in Toronto public housing, about what makes a community. Feminist participatory action research (FPAR) and narrative methods are briefly reviewed in this paper as they are influential to the trajectory of presenting this data. The voices of the women who engaged in the project of focus were heard, without compromise, vis-a-vis the approaches and method we used to conduct our research and to communicate their stories to the conference audience. As a doctoral student researcher, I aimed to present this project at the conference in a way that aligned with social justice principles of FPAR, particularly the notion of “power-with,” as discussed by Ponic, Reid, and Frisby (2010). In disclosing this process, I hope to provide insight, in a clear and accessible fashion, to others who will conduct and present participatory research in similar settings.
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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 | 0.199 | 0.222 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.017 | 0.066 |
| Scholarly communication | 0.023 | 0.019 |
| Open science | 0.005 | 0.026 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".