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Record W2118258686 · doi:10.1111/caje.12017

Quality of life, firm productivity, and the value of amenities across Canadian cities

2013· article· en· W2118258686 on OpenAlexaffvenueabout
David Albouy, Fernando Leibovici, Casey Warman

Bibliographic record

VenueCanadian Journal of Economics/Revue canadienne d économique · 2013
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicRegional Economics and Spatial Analysis
Canadian institutionsDalhousie University
Fundersnot available
KeywordsMetropolitan areaCensusProductivityWageGeographyLand ValuesValue (mathematics)Agricultural economicsDemographic economicsSocioeconomicsEconomicsEconomic growthLabour economicsLand useDemographySociologyPopulation

Abstract

fetched live from OpenAlex

Abstract We estimate quality‐of‐life and productivity differences across Canada's metropolitan areas in a hedonic general‐equilibrium framework. These are based on the estimated willingness‐to‐pay of heterogeneous households and firms to locate in various cities, which differ in their wage levels, housing costs, and land values. Using 2006 Canadian Census data, our metropolitan quality‐of‐life estimates are somewhat consistent with popular rankings, yet find Canadians care more about climate and culture. Quality of life is highest in Victoria for anglophones, Montreal for francophones, and Vancouver for allophones, and lowest in more remote cities. Toronto is Canada's most productive city; Vancouver is the overall most valuable city.

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 score0.023
Threshold uncertainty score0.168

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0030.008
Science and technology studies0.0020.001
Scholarly communication0.0030.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.133
GPT teacher head0.194
Teacher spread0.061 · 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

Citations47
Published2013
Admission routes3
Has abstractyes

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