L’estimation de l’emploi à partir de la distribution des établissements par taille
Bibliographic record
Abstract
La rareté des données est un problème endémique dans les recherches économiques appliquées à l’échelle régionale, a fortiori à l’échelle inframétropolitaine. Cet article discute trois méthodes pragmatiques d’estimation de l’emploi à partir de la distribution des établissements par taille : la méthode du point milieu de l’intervalle (méthode PM), le lissage lognormal et le lissage log-logistique. On compare les méthodes au moyen de deux ensembles de données : celles du Recensement des établissements et de l’emploi de Montréal de 1996 (Banque de Données et d’Information urbaine, INRS-Urbanisation et Ville de Montréal) et celles de Statistique Canada sur les industries manufacturières à deux chiffres de la Classification Type des Industries (CTI) au Québec en 1995. Les résultats montrent que les modèles lognormal et logistique sont nettement supérieurs à la méthode PM, notamment à cause du biais à la hausse inhérent à cette méthode.
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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 | 0.021 | 0.087 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".