Bibliographic record
Abstract
L’article retrace le processus par lequel la microsimulation, nouvelle forme d’évaluation ex ante , s’est imposée comme l’instrument privilégié pour concevoir et anticiper des effets d’une réforme du système socio-fiscal en France. Principalement conçus au sein des administrations « chiffreuses », les modèles de microsimulation sont d’abord élaborés de façon non coordonnée, appelant des jeux de concurrence mais aussi de coopération entre les équipes et institutions en charge de leur développement. Une nouvelle étape s’amorce à la fin des années 2000. Le monopole quasi exclusif de l’administration sur la microsimulation s’érode avec le retour progressif des économistes académiques dans le domaine. En découle une recomposition du paysage de la microsimulation qui, malgré la subsistance d’entreprises concurrentes, affirme un monopole de l’instrument sur la façon de penser une réforme du système socio-fiscal.
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 | 0.006 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.015 | 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".