MétaCan
Menu
Back to cohort
Record W7124944185 · doi:10.1111/1759-3441.70005

The Role of Work Experience Programmes in Shaping Employment Outcomes for Indigenous Peoples in Canada

2025· article· en· W7124944185 on OpenAlexaffabout
Laura Lamb, Mushran Siddiqui, Stefania Strantza

Bibliographic record

VenueEconomic Papers A journal of applied economics and policy · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicIndigenous Health, Education, and Rights
Canadian institutionsThompson Rivers University
Fundersnot available
KeywordsIndigenousWorkforceHuman capitalWork (physics)InternshipWork experience

Abstract

fetched live from OpenAlex

This study examines employment outcomes of Indigenous peoples in Canada using an expanded human capital framework that includes education, health and work experience, such as internships and cooperative programmes. Despite some improvements, Indigenous employment rates remain below those of non‐Indigenous Canadians, with disparities across First Nations, Métis and Inuit populations. Using data from the 2016 Aboriginal Peoples Survey, this research assesses the impact of work experience programmes on employment status and income. Results show participation in work experience programmes increases the likelihood of employment by 12 percent, while post‐secondary education and good health also improve employment prospects. Findings highlight the need to broaden human capital strategies to include work experience programmes. These results are of particular interest to policymakers in Canada and Australia seeking evidence‐based strategies to improve employment outcomes and reduce economic disparities among Indigenous populations. This study is the first to empirically assess the role of work experience in Indigenous employment outcomes in Canada and provides new evidence to support Indigenous workforce development through experiential learning and holistic human capital investment.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.632
Threshold uncertainty score0.976

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.009
GPT teacher head0.277
Teacher spread0.268 · 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 designQualitative
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

Citations0
Published2025
Admission routes2
Has abstractyes

Explore more

Same venueEconomic Papers A journal of applied economics and policySame topicIndigenous Health, Education, and RightsFrench-language works237,207