What's the problem with gender‐based analysis? Gender mainstreaming policy and practice in Canada
Bibliographic record
Abstract
Abstract: Gender “mainstreaming” is an important concept in feminist politics because it integrates a gendered perspective into all policy‐ and decision‐making. However, while most scholars agree that gender mainstreaming has the potential to transform social relations, to date it has been limited and delivered only marginal benefits for a few women. In the Canadian context, scholars have pointed to several contextual and conceptual issues that limit the transformative potential of gender‐based analysis. While such studies have contributed to our understanding of the impacts of gender mainstreaming, the author suggests that we must also explore the creative or productive dimensions of mainstreaming. When we do so, we see that gender mainstreaming constructs a new form of worker: the “gender expert,” who is then given authority to analyse, monitor and suggest interventions based on “expert analysis.” From this perspective, gender analysis becomes a “technology of rule,” constructing gender experts whose power ultimately goes unscrutinized in the context of the organization, thus obscuring the ways in which gender systems are reproduced or fractured by gender mainstreaming itself. In closing, the author calls for a reorientation of gender mainstreaming, away from an analytic approach that focuses only on the instrumental effects of policies and towards an approach that illuminates both the instrumental and creative impacts of policies.
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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 | 0.027 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.011 |
| Science and technology studies | 0.050 | 0.031 |
| Scholarly communication | 0.022 | 0.006 |
| Open science | 0.006 | 0.008 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.007 | 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".