MétaCan
Menu
Back to cohort

Development similarity based on proximity: A case study of urban clusters in Canada

2004· article· en· W2000461784 on OpenAlexaffabout
Boris A. Portnov, Barry Wellar

Bibliographic record

VenuePapers of the Regional Science Association · 2004
Typearticle
Languageen
FieldSocial Sciences
TopicUrban Transport and Accessibility
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsGeographyEconomic geographySimilarity (geometry)UnemploymentCluster analysisPopulationCluster (spacecraft)Demographic economicsRegional scienceEconomic growthEconomicsDemographySociologyStatisticsMathematics

Abstract

fetched live from OpenAlex

Abstract. Neighbouring towns in urban clusters of Canada exhibit similar levels of socio-economic development. However, when measured by different development indicators, inter-town development association differs in both nature and degree. In core areas, for instance, only population and housing variables exhibit a strong spatial association, while that of employment-related variables – average income, and unemployment rate – is weaker. This tendency reflects the fundamental difference between the two groups of variables. While population and housing variables are associated with the clustering of residents in socially homogenous areas, inter-town development similarity in respect to employment-related variables is weaker, apparently due to long-distance commuting. The article discusses the importance of urban clustering as a factor in regional development policies and programmes, and provides support for including cluster-related elements in a strategy to enhance urban growth in underdeveloped regions.

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)0.006
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 score0.028
Threshold uncertainty score0.205

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.010
Science and technology studies0.0080.003
Scholarly communication0.0030.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.023
GPT teacher head0.269
Teacher spread0.246 · 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

Citations12
Published2004
Admission routes2
Has abstractno

Explore more

Same venuePapers of the Regional Science AssociationSame topicUrban Transport and AccessibilityFrench-language works237,207