Bibliographic record
Abstract
Drawing on a recent Canadian study which uses an intersectional disability and gender policy analysis framework, this chapter answers: how did you do it? How did it work? And how might you do it differently knowing what you know now? This chapter discusses some of the tensions that we have experienced in this project and how we addressed the challenges. On reflection from these experiences, the chapter argues that transformational policy change requires critical and intersectional policy analysis consistently used from the beginning of the idea through to the dissemination of results, recognising and addressing the inherent power relations at work in both the research and the policies examined. Moving to transformative change also requires researchers to work together with civil society partners to undertake and share the research and policy alternatives in ways that enhance and showcase the intersectional analysis and practice.
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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 | 0.016 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.026 | 0.041 |
| Scholarly communication | 0.025 | 0.023 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.009 | 0.021 |
| Insufficient payload (model declined to judge) | 0.026 | 0.009 |
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".