Microsimuler l’avenir des retraites en France : l’exemple du modèle Destinie
Bibliographic record
Abstract
Le modèle Destinie est un modèle de microsimulation dynamique dont la construction avait débuté à l’INSEE dans les années 1990. Son champ d’application principal est la prospective des retraites. Il a été notamment mobilisé pour éclairer les effets des réformes intervenues en France depuis 1993. Cet article présente le contexte dans lequel sa première version a été construite, les principaux choix qui ont été retenus pour cette construction et les diverses applications qui en ont été faites. Il dresse un bilan de ces applications et présente les améliorations apportées par la nouvelle version du modèle, opérationnelle depuis 2011.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".