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Record W2021238839 · doi:10.1093/jeg/lbr017

Cities, skills and wages

2011· article· en· W2021238839 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Economic Geography · 2011
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicRegional Economics and Spatial Analysis
Canadian institutionsOntario Brain Institute
Fundersnot available
KeywordsProsperityLibrary scienceSchools of economic thoughtManagementPolitical scienceEconomic historyMedia studiesSociologyHistoryEconomicsLaw

Abstract

fetched live from OpenAlex

This research examines the effect of skills in cities on regional wages. We use cluster analysis to identify three broad skill types—analytical, social intelligence and physical skills from 87 occupational skills. We examine how each skill contributes to regional wages and how they are related to regional size, using data from 1999 and 2008. We find that analytical and social intelligence skills have a significant positive effect on regional wages, while physical skills have a negative effect. Analytical skills are also somewhat more closely associated with regional wages than social intelligence skills, after controlling for education, industry, immigration and regional size. Furthermore, wage return to analytical and social intelligence skills has increased over time, and the return to physical skills has declined significantly. We also show that larger cities reward analytical and social intelligence skills to a higher degree, whereas smaller cities rely more on physical skills.

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.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.202
Threshold uncertainty score0.921

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.021
GPT teacher head0.187
Teacher spread0.166 · 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