Best Laid Plans: Examining Contradictions between Intent and Outcome in a Feminist, Collaborative Research Project
Bibliographic record
Abstract
This article critically examines a feminist, collaborative research method that was intended to be political in standpoint, gendered in focus, reflexive in process, and transformative in outcome. By incorporating collaborative elements into a qualitative, three-step research design, the author hoped to challenge both what was known about nurses' job displacement and how that knowledge was produced. This article explores the contradictions between the author's best laid plans and the actual process of discovery. Recommendations for future research include considerations about the social and political context in which the research takes place, cautions about gender inclusivity in the research population and analytic frameworks, strategies for encouraging participants' critical thinking, and a caveat with regard to transformative outcomes.
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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 | 0.184 | 0.361 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.015 | 0.056 |
| Scholarly communication | 0.018 | 0.021 |
| Open science | 0.004 | 0.021 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".