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La dynamique spatiale des marchés locaux de l'emploi au sein du champ métropolitain de Québec, 1981–2001

2007· article· en· W1587742323 on OpenAlexaffvenueabout
Rémy Barbonne, Paul Villeneuve, Marius Thériault

Bibliographic record

VenueCanadian Geographies / Géographies canadiennes · 2007
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicRegional Economics and Spatial Analysis
Canadian institutionsUniversité LavalInstitut National de la Recherche Scientifique
Fundersnot available
KeywordsMetropolitan areaGeographyWorkforceEconomic geographyEconomicsEconomic growth

Abstract

fetched live from OpenAlex

Spatial Dynamics of Local Labour Markets in the Québec City Metropolitan Field, 1981–2001 This research analyzes the spatial dynamics (from 1981 to 2001) of local labour markets at an infra‐regional scale, namely the Québec metropolitan field, with particular emphasis on interactions between the metropolitan region and its hinterland. It seeks to better understand the factors underlying this evolution. Centrographic analyses were performed to characterize the evolution of the spatial configuration of local labour markets (displacement of gravity centre, shape change, evolution of dispersion indices and of workforce preferential distribution axes). Between 1981 and 2001, almost all employment poles experienced an increase in the mean‐distance tied to their recruitment area, that being particularly true for peri‐metropolitan poles which employ an increasing part of their workforce inside the metropolitan labour basin, where a more qualified and diversified labour force is available; thus, giving rise to significant reverse commuting. In addition to the influence of distance to metropolitan area, a multiple regression model shows that factors such as manufacturing specialization and employment growth within job centres also play a crucial role in the spatial dynamics of local labour markets in the Québec City metropolitan field .

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.174
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0050.003
Science and technology studies0.0010.002
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.015
GPT teacher head0.196
Teacher spread0.182 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations6
Published2007
Admission routes3
Has abstractyes

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