Bibliographic record
Abstract
Dans cet article, nous présentons un modèle de microsimulation qui permet de générer des histoires de vie individuelles cohérentes avec les projections de cohortes obtenues par le modèle multi-états à composantes de cohorte. Le modèle de microsimulation peut prendre en compte des différences individuelles que le modèle à composantes de cohorte ne peut traiter. Ces différences sont dues à une association de facteurs systématiques et de facteurs aléatoires. Nous nous intéressons principalement aux facteurs aléatoires. Le modèle de microsimulation utilise un modèle de survie de cohorte multi-états à temps continu et des tirages aléatoires à partir de distributions de temps d’attente exponentielles par morceaux. Si on prend comme paramètres des distributions les intensités de transition du modèle de survie de cohorte, les histoires de vie individuelles sont cohérentes avec les projections de cohorte. Les histoires de vie apportent un éclairage sur la dynamique démographique que la méthode des composantes ne peut proposer.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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; 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".