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Record W3205925098 · doi:10.1080/13639080.2021.1989574

The postsecondary qualifications of Canadian workers: disparities and bottlenecks in regulated, applied, and general occupations

2021· article· en· W3205925098 on OpenAlexaffabout
Eric Lavigne, Lindsay Coppens, Juliette Sweeney, Gavin Moodie, Ruth A. Childs, Leesa Wheelahan

Bibliographic record

VenueJournal of Education and Work · 2021
Typearticle
Languageen
FieldHealth Professions
TopicEmployment and Welfare Studies
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsCognitive reframingHuman capitalTechnicianHuman capital theoryPostsecondary educationLabour economicsApprenticeshipWork (physics)Vocational educationHigher educationDemographic economicsSociologyPolitical scienceEconomicsPsychologyPedagogyEconomic growthEngineeringSocial psychologyLaw

Abstract

fetched live from OpenAlex

This article reports on a study investigating the link between education and work. Instead of looking at the labour outcomes of graduates, the study examined the qualifications held by workers in technician- and professional-level jobs from three types of occupational fields: regulated, applied, and general. The approach shifts the focus away from the supply of qualifications to the way qualifications are used in the workplace. The findings show evidence of disparities between qualifications and work levels and of the presence of obstacles preventing workers with additional qualifications from securing access to better jobs. Overall, the findings show that the structure of the labour market shapes how workers and employers make use of qualifications. They highlight some of the limitations of human capital theory in explaining the links between education and the labour market and call for a reframing of the purpose of postsecondary qualifications.

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)0.002
metaresearch head score (Gemma)0.005
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 score0.029
Threshold uncertainty score0.211

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.007
Science and technology studies0.0070.002
Scholarly communication0.0020.001
Open science0.0020.003
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.037
GPT teacher head0.373
Teacher spread0.336 · 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

Citations1
Published2021
Admission routes2
Has abstractyes

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