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Canadian cities as regional engines of growth: agglomeration and amenities

2007· article· en· W1545569544 on OpenAlexaffvenueabout
Mark D. Partridge, M. Rose Olfert, Alessandro Alasia

Bibliographic record

VenueCanadian Journal of Economics/Revue canadienne d économique · 2007
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicRegional Economics and Spatial Analysis
Canadian institutionsStatistics CanadaUniversity of Saskatchewan
Fundersnot available
KeywordsEconomic geographyMetropolitan areaEconomies of agglomerationAmenityGeographyPopulation growthPopulationUrban agglomerationDevelopment economicsDemographic economicsEconomic growthEconomicsDemographySociology

Abstract

fetched live from OpenAlex

Abstract. Canadian regional population growth is less understood than that of the United States. In both countries, certain regions have persistent population growth. Yet, unlike U.S. trends of amenity‐driven migration away from historic urban centres, Canadian growth is more urban centric. This study assesses whether agglomeration economies in the few major Canadian metropolitan areas lead to population growth in or near these cities rather than the more‐dispersed U.S. patterns. The results suggest that disparities such as the concentration of Canadians along its southern border may explain migration patterns, indicating that key differences in initial conditions may produce different outcomes between the two countries.

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)0.002
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 score0.043
Threshold uncertainty score0.140

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0030.006
Science and technology studies0.0020.002
Scholarly communication0.0030.001
Open science0.0010.002
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0080.000

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 head0.098
GPT teacher head0.170
Teacher spread0.072 · 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

Citations77
Published2007
Admission routes3
Has abstractyes

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