How Could a Gender Transformative Lens Foster the Integration of Sex/Gender into More Equitable Policy and Practice?
Bibliographic record
Abstract
Gender transformative policies and practices address underlying gender inequities and respond to specific social, health, or economic problems. Gender transformative approaches have historically focused on improving women’s status by changing gendered power relations, redefining masculinities, and exhorting communities and institutions to address the drivers and root causes of problems. Sex and gender entanglement poses a challenge to making policies and practices that can better reflect gender transformative approaches in that such approaches need to be robust, precise, and based on evolving science. This chapter proposes expansions of theory and practice to progress gender transformative approaches that reflect both sex/gender entanglement and engage all gender groups in efforts to reduce gender inequity. Achieving these goals requires (re)committing to feminism, engaging as critically with femininities as masculinities, integrating corporeality, and recognizing individual and collective agency in responding to hegemonic gender. These actions need to recognize ongoing and evolving impacts of sex and gender and sex/gender entanglement. This approach will facilitate improvements in policy and support new areas of gender transformative practice that can be operationalized with more precision in a proportionate universalism framework that differentially attends to groups based on need and disadvantage. Examples of gender transformative policy approaches consider entanglements of sex/gender in regulatory, communication, and policy activities aimed at reducing gender inequity.
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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 | 0.007 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.041 |
| Scholarly communication | 0.012 | 0.015 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".