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La ségrégation économique comme un phénomène complexe : une analyse spatio‐temporelle du cas montréalais

2005· article· en· W1990007521 on OpenAlexaffvenueabout
Mathieu Charron, Richard Shearmur

Bibliographic record

VenueCanadian Geographies / Géographies canadiennes · 2005
Typearticle
Languageen
FieldSocial Sciences
TopicUrban Transport and Accessibility
Canadian institutionsInstitut National de la Recherche Scientifique
Fundersnot available
KeywordsMetropolitan areaCensusGeographyEconomic geographyWelfare economicsEconomicsSociologyDemographyArchaeologyPopulation

Abstract

fetched live from OpenAlex

Economic segregation, or residential inequalities based on income, is a complex spatial process. It is associated with structures which arise at certain scales and which evolve in time. In this paper, we analyse the evolution of economic segregation in Montréal from 1980 to 2000. This analysis is based on household's income and on their distribution within the residential space from four scales: households, census tracts, municipalities and some radio‐concentric zones. Results confirm that the spatial organisation of economic segregation is scale dependent. More precisely, local inequalities are growing more rapidly but this trend does not prevent the consolidation of metropolitan‐sized clusters. Moreover, results confirm that Montréal's economic segregation follows North American trends like the growth of economic disparities (spatial or not) or the fact that the enrichment of central and ex‐urban neighbourhoods is compensated by the impoverishment of inner‐ring suburbs.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.079
Threshold uncertainty score0.159

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.005
Science and technology studies0.0020.003
Scholarly communication0.0040.002
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.018
GPT teacher head0.229
Teacher spread0.210 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations7
Published2005
Admission routes3
Has abstractyes

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