Working in Higher Education in France Today: A Specific Challenge for Women
Bibliographic record
Abstract
By 2017, French higher education had undergone a dramatic restructuration following the Bologna process twenty years earlier which impact all the European universities (Rüegg, 2010), and the implementation of the French LRU in 2007 (Stavrou, 2017). Some studies examined this new model’s effect on university academics through international or european comparative approaches (Musselin, 2008 ; Tiechler, Höhle, 2013). A decade after the French LRU, our particular focus concerns the activity of women with children like in others organizations (Bercot, 2014). The associate professors have to overcome in a very competitive context where the time management is a real challenge as the 3 coordinators at different levels in the faculty point it out. At first, an extensive survey (1409 returned questionnaires) shows that women are significantly more concern than men by these contraints. Then, in a qualitative approach, some 28 biographical interviews identify the different strategies women find.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.003 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".