Les cabinets ministériels fédéraux : effectif, recrutement et profil des « chefs »
Bibliographic record
Abstract
Cet article présente d’abord un portrait statistique des membres des cabinets ministériels fédéraux. Il s’intéresse ensuite au mode de recrutement des premiers responsables des cabinets ministériels, les « chefs de cabinet ». L’article trace également le profil socio-professionnel de ces chefs. L’examen des caractéristiques sociologiques et professionnelles montre que les chefs sont issus de milieux diversifiés, qu’ils sont assez bien formés et plutôt jeunes. De plus, cet examen contredit la prévision selon laquelle les chefs proviennent des milieux privés d’affaires plus sympathiques aux idées néo-conservatrices. De fait, le recrutement des chefs se fait dans plusieurs milieux dont au premier chef la filière partisane. De ce point de vue, il n’y a pas un moule et le groupe des chefs de cabinet ne forme ni une dynastie, ni une chapelle ou un club fermé qui ne serait accessible qu’à un petit nombre.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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; both teacher heads agree on what is shown here.
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".