La délocalisation des emplois de services supérieurs : le cas de la RMR de Montréal 1996-2011
Bibliographic record
Abstract
Depuis une trentaine d’années, géographes et économistes ont alimenté la littérature scientifique de la forme urbaine. À partir de la décennie 1980, certaines métropoles étatsuniennes ont fait face à une délocalisation des emplois de services supérieurs partant des centres-villes vers des pôles périphériques. Pour le cas de la région de Montréal, plusieurs études ont observé que la métropole québécoise avait été épargnée de cette décentralisation, et ce, jusqu’au tournant du XXIe siècle. Dans ce mémoire, nous continuerons d’analyser l’évolution de la répartition spatiale dans la RMR de Montréal entre les années 1996 et 2011. À partir de microdonnées de Statistiques Canada, nous avons identifié plus d’une quinzaine de pôles d’emplois de services supérieurs. Nos résultats indiquent que le nombre d’emplois de services supérieurs du centre-ville de Montréal s’accroît continuellement en terme absolu, mais diminue en terme relatif au profit des zones hors-pôles du reste de la RMR. Over the last thirty years, geographers and economists have produced an abundant literature on urban form. Since the 1980s, many metropolitan areas in the United States saw high-order service employment decentralize from the CBD to the suburbs. Studies for the Montreal metropolitan area suggest that Quebec’s metropolis has been largely spared by the decentralization process; at least until the dawn of the 21st century. In this master's thesis we analyze the evolution of the spatial distribution of high-order service employment in the Montreal CMA between 1996 and 2011. Using Statistics Canada microdata we identify sixteen high-order service employment centers. Our results show that the number of high-order service jobs in downtown Montreal is increasing in absolute terms, but decreased in relative terms, the main beneficiaries being areas outside employment centers.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".