Villes et croissance : Migration a Toronto - croissance du revenu associee au marche du travail des grandes regions metropolitaines
Bibliographic record
Abstract
La presente etude porte sur le processus amenant les migrants a realiser des gains salariaux a la suite de leur migration; l'examen porte plus particulierement sur l'avantage associe a la migration vers des marches du travail metropolitains dynamiques et de grande taille, Toronto servant a cet egard de point de reference. On peut observer deux profils de gains distincts associes a la migration. Le premier correspond a une augmentation immediate du revenu du travailleur a la suite de la migration. Le second consiste en une progression acceleree du revenu apres la migration. Le gain immediat est associe a l'obtention d'un poste au sein d'une entreprise plus productive, ou encore a un meilleur appariement entre les competences et habiletes du travailleur et les taches associees a son poste. L'acceleration du gain de revenu est pour sa part rattachee a des processus qui exigent un certain temps, par exemple l'apprentissage ou le changement d'emploi au fil de la recherche d'une plus grande compatibilite entre travailleurs et entreprises. Notre evaluation porte ici sur l'hypothese que les economies reliees aux grandes regions metropolitaines permettent aux travailleurs de profiter au depart d'un avantage decoulant d'une hausse ponctuelle de productivite et/ou d'un processus dynamique permettant d'accelerer la progression de leur revenu grace a l'apprentissage et a un meilleur appariement. Divers ensembles de donnees et methodologies, y compris la methode de l'appariement par scores de propension, servent a evaluer les profils de progression du revenu associes a la migration a Toronto.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".