Gender mainstreaming et approche différenciée selon les sexes: quels impacts pour la gestion?
Bibliographic record
Abstract
The goal of this text is to situate gender mainstreaming in relation to other plans of action for gender equality, such as equal opportunity initiatives and policies, positive discrimination, affirmative action, and diversity management programs. Of course, there are similarities in the action plans, but there are also differences regarding the eras in which they came to be, the analyses on which they are based, the objectives sought, the courses of action drawn up, and the implications for HRM. Looking at writings and practices, this text attempts to clarify these concepts, to specify their objectives, to explore the resulting courses of action at the macro level (public policy) and at the micro level (human resources management within organizations), and to zero in on the zones of debate that cut across them. [PUBLICATION ABSTRACT]
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 | 0.033 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.008 | 0.049 |
| Scholarly communication | 0.020 | 0.023 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".