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Record W3165973152 · doi:10.1177/00221856211021128

Nonstandard Employment and Indigenous Earnings Inequality in Canada

2021· article· en· W3165973152 on OpenAlexaffabout
Danielle Lamb, Anil Kumar Verma

Bibliographic record

VenueJournal of Industrial Relations · 2021
Typearticle
Languageen
FieldHealth Professions
TopicEmployment and Welfare Studies
Canadian institutionsUniversity of TorontoToronto Metropolitan University
Fundersnot available
KeywordsIndigenousEarningsInequalityHuman capitalWork (physics)Labour economicsEconomicsDemographic economicsEconomic growthFinance

Abstract

fetched live from OpenAlex

The study investigates the extent to which the type of employment, specifically nonstandard work, may contribute to a better understanding of Indigenous earnings disparities. We find that Indigenous workers are overrepresented in nonstandard jobs and that such forms of work are associated with sizable earnings penalties. Although Indigenous earnings disparities are smaller in nonstandard work than in standard employment, the relatively low earnings of many nonstandard jobs are an important factor contributing to the overall economic inequalities experienced by many Indigenous Canadians. Policy responses aimed at improved human capital accumulation are likely to have limited efficacy unless additional barriers that prevent many Indigenous workers from accessing better quality employment and internal labor markets are identified and removed.

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)0.001
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 score0.445
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.092
GPT teacher head0.379
Teacher spread0.287 · 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.

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

Citations11
Published2021
Admission routes2
Has abstractyes

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