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Record W1594250290 · doi:10.1111/ecge.12056

On the Relationship between Innovation and Wage Inequality: New Evidence from<scp>C</scp>anadian Cities

2014· article· en· W1594250290 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueEconomic Geography · 2014
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicRegional Economics and Spatial Analysis
Canadian institutionsMcGill University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsInequalityEarningsCensusEconomicsGovernment (linguistics)PopulationSustainabilityDistribution (mathematics)Demographic economicsSociologyDemography

Abstract

fetched live from OpenAlex

Abstract In this article, we examine the link between innovation and earnings inequality across C anadian cities over the 1996–2006 period. We do so using a novel data set that combines information from the C anadian long‐form census and the U nited S tates P atent and T rademark O ffice. The analysis reveals that there is a positive relationship between innovation and inequality: cities with higher levels of innovation have more unequal distributions of earnings. Other factors influencing differences in inequality include city size, manufacturing and government employment, the percentage of visible minority in an urban population, and educational inequality. These results are robust to the use of different measures of inequality, innovation, alternative specifications, and instrumental variables estimations. Questions are thus raised about how the benefits of innovation are distributed in society and the long‐term sustainability of such trends.

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)0.001
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 score0.022
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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)0.0000.001

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 head0.079
GPT teacher head0.232
Teacher spread0.152 · 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