Stories from the Front Lines: Making Sense of Gender Mainstreaming in Canada
Bibliographic record
Abstract
Gender mainstreaming (GM) is a strategy used by governments to promote gender equality. It entails integrating gender and intersectional considerations into all aspects of policy work, including policy formulation, implementation, and evaluation. However, its success in achieving gender equality and social transformation has been limited. Drawing on implementation research and narrative analysis, this article explores the micro-level dynamics and the local actors that help shape the character and outcome of gender mainstreaming. Using narrative analysis, we explore how GM specialists within the Canadian public service make sense of their role, and we identify the strategies they use to make gender matter in policy work. By examining their stories of isolation, disempowerment, and resistance, we uncover the administrative and political forces that shape not only the “space” for gender work but also the opportunities for individual activism and resistance. These stories convey how, by engaging in these micro-level strategies, GM specialists both challenge and reinscribe, at the macro level, technocratic representations of GM and of policy work in general. We conclude with some reflections on the insights that micro-level analysis and implementation research can bring to the study of gender mainstreaming.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.070 | 0.032 |
| Scholarly communication | 0.017 | 0.007 |
| Open science | 0.005 | 0.012 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.006 | 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".