Bibliographic record
Abstract
This article articulates many of the issues that feminist participatory action researchers confront in attempts to conduct collaborative research with community organizations and the state (see Brydon-Miller, McGuire, & McIntyre, 2004; Gatenby & Humphries, 2000; Reid, Tom, & Frisby, 2006; Sullivan, Bhuyan, Senturia, Shiu-Thornton, & Ciske, 2005). As recent PhD sociologists, the authors were hired as independent consultants by a provincial ministry 1 to evaluate an initiative to expand service provision to women who had experienced violence by their intimate partners. 2 Our analysis of what transpired during this consultancy experience is grounded in our participant observation and a reflective process in which we have engaged, periodically, over the past 10 years. During that time we have articulated, and re-articulated our `story', both informally and formally, through solitary and collaborative writing and rewriting endeavors. Our immersion in this process has yielded ever-evolving understandings of this life experience, and the passage of time has allowed us to refine an analysis because of the distance in time between now and our involvement. We begin by outlining our understanding of feminist participatory action research (FPAR) that informed our work with the ministry, followed by our story of what happened and our sociological analysis of that story.
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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 | 0.031 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.038 | 0.046 |
| Scholarly communication | 0.018 | 0.017 |
| Open science | 0.005 | 0.048 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.011 | 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; 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".