Prospective démographique localisée : de la diversité des méthodes à l’importance des données de recensement
Bibliographic record
Abstract
La prospective démographique localisée est susceptible de recouvrir plusieurs démarches distinctes. Cette diversité méthodologique renvoie à la fois à des différences de degré d’élaboration et de respect des exigences de cohérence interne et au contexte qui a conduit à projeter une ou plusieurs populations locales. Après avoir présenté les principales méthodes de projection démographique utilisées en démographie locale, ce texte explique et illustre, par un exemple caractéristique, la façon dont elles peuvent être articulées pour répondre aux demandes de plus en plus composites des collectivités territoriales. Il décrit ensuite les données de recensement nécessaires à la mise en oeuvre de ces démarches de prospective démographique localisée en fonction de leur degré d’élaboration.
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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 | 0.057 | 0.148 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.011 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".