Bibliographic record
Abstract
Pour mettre en œuvre ses politiques publiques, l’Etat compte sur un appareil administratif regi par un grand nombre de regles internes qui peuvent causer des interferences avec la mise en œuvre des politiques publiques et mettre en cause leur coherence. Y a-t-il interference entre la mise en œuvre des politiques publiques et les regles internes des ressources humaines? Pour y repondre, nous avons etudie le cas d'un grand ministere canadien. Dans le cadre d’une demarche inductive et par le truchement d’une analyse documentaire, nous avons constate que la mise en œuvre des politiques publiques pouvait etre influencee negativement par un mecanisme de tensions agissant sur l’application des regles internes. Nous avons d’abord etabli que l’acteur etait constitue d’un couple, gestionnaire-conseiller. Ce couple sous tension interpretait la regle sous l’effet d’un conditionnement de groupe, propre aux organisations publiques et domine par l’element conseiller. Par contre, cet equilibre interne au couple a bascule lorsque les pressions contraires ont pousse le couple acteur a conclure qu’il y avait plus d’avantages a desobeir a la regle interne qu’a lui obeir.
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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.009 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.017 | 0.002 |
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".