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Record W2282957970 · doi:10.1111/caje.12145

Has the Canadian labour market polarized?

2015· article· en· W2282957970 on OpenAlexaffvenueabout
David A. Green, Benjamin Sand

Bibliographic record

VenueCanadian Journal of Economics/Revue canadienne d économique · 2015
Typearticle
Languageen
FieldHealth Professions
TopicEmployment and Welfare Studies
Canadian institutionsYork UniversityUniversity of British Columbia
Fundersnot available
KeywordsPolarization (electrochemistry)BoomEconomicsWageLabour economicsTechnological changeCensusInequalityWage growthWage inequalityDemographic economicsMacroeconomicsPopulationSociology

Abstract

fetched live from OpenAlex

Abstract We use Census and Labour Force Survey (LFS) data for the period from 1971 to 2012 to investigate whether the Canadian wage and employment structures have polarized, that is, whether wages and employment have grown more in high‐ and low‐ than in middle‐paying occupations. We find that there has been faster growth in employment in both high‐ and low‐paying occupations than those in the middle since 1981. However, up to 2005, the wage pattern reflects a simple increase in inequality with greater growth in high‐paid than middle‐paid occupations and greater growth in middle than low‐paid occupations. Since 2005, there has been some polarization but this is present only in some parts of the country and seems to be related more to the resource boom than technological change. We present results for the US to provide a benchmark. The Canadian patterns fit with those in the US and other countries apart from the 1990s when the US undergoes wage polarization not seen elsewhere. We argue that the Canadian data do not fit with the standard technological change model of polarization developed for the US.

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 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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.642
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.290
GPT teacher head0.273
Teacher spread0.018 · 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 teacher head, not a consensus.

Study designNot applicable
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

Citations85
Published2015
Admission routes3
Has abstractyes

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