Bureaucratic Role Perceptions and Gender Mainstreaming in Canada
Bibliographic record
Abstract
Since the mid‐1990s, governments have adopted gender mainstreaming (GM) as a strategy for achieving gender equality and improving women's social, economic and political conditions. Yet, studies indicate that GM continues to be unevenly implemented, both within and across countries. To explain this outcome, this paper focuses on the local implementers of GM — the gender focal points — and how they understand GM and interpret it in their everyday work. Drawing upon interviews with gender focal points in the Canadian public service, we explore how bureaucratic role perceptions shape how these local actors understand GM and how they navigate the complex terrain between bureaucratic neutrality and the equality agenda of gender mainstreaming. Our exploratory study shows no common understanding among our interviewees, revealing how the meaning of gender mainstreaming varies depending on whether the public servant views himself or herself as policy analyst, policy advisor or policy advocate. Based on these insights, we conclude with suggestions for future research on 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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.049 | 0.026 |
| Scholarly communication | 0.011 | 0.002 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".