La distribution des taux de croissance de l'emploi au Canada : le rôle des entreprises à forte croissance et à réduction rapide des effectifs
Bibliographic record
Abstract
La presente etude s?appuie sur des donnees provenant de la base de donnees du Programme d?analyse longitudinale de l?emploi de Statistique Canada pour examiner la distribution des taux de croissance de l?emploi au Canada de 2000 a 2009, en se concentrant surtout sur les entreprises situees dans les queues de la distribution, appelees ici entreprises a forte croissance (EFC) et entreprises a reduction rapide des effectifs (ERR). L?etude a trois objectifs. Le premier consiste a decrire les distributions des taux de croissance de l?emploi au Canada afin de voir si elles concordent avec celles observees dans d?autres pays. Le deuxieme consiste a quantifier la contribution des EFC et des ERR a la creation ainsi qu?a la destruction agregees des emplois. Enfin, le troisieme consiste a examiner, en faisant appel a des techniques de regression quantile, le role de la taille et de l?age des entreprises dans le rendement des EFC et des ERR.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".