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Record W4367153809 · doi:10.7202/1096443ar

Postsecondary Inuit Students From Nunavut Pathways: When Students’ Satisfaction Meets Language Discrimination

2023· article· en· W4367153809 on OpenAlexafffundvenueabout
Thierry Rodon, Jean-Luc Ratel, Pamela Hakongak Gross, Francis Lévesque, Maatalii Okalik

Bibliographic record

VenueMcGill Journal of Education / Revue des sciences de l éducation de McGill · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicMultilingual Education and Policy
Canadian institutionsNunavut SivuniksavutUniversité du Québec en Abitibi-TémiscamingueUniversité du Québec à MontréalUniversité Laval
FundersNunavut General Monitoring PlanUniversité Laval
KeywordsPostsecondary educationWork (physics)InequalityPsychologyHigher educationEducational attainmentEconomic growthEconomics

Abstract

fetched live from OpenAlex

We present a multiple correspondence analysis (MCA) based on a survey of 362 Inuit students and graduates from Nunavut who attended college or university in Canada. Most participants reported that they were satisfied with their postsecondary educational experience and that postsecondary education had greatly improved their income and job outcomes. Results also show that postsecondary education clearly contributes to capacity building: Half of the participants reported working in their communities, and a majority said they wanted to work there. Nevertheless, some issues still need to be addressed by policymakers, the most notable being gender inequality in terms of job status, systemic discrimination against Inuit language speakers in the educational system, and the need to provide more access to postsecondary education.

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)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.415
Threshold uncertainty score0.836

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0040.001
Scholarly communication0.0030.001
Open science0.0010.002
Research integrity0.0010.001
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 head0.367
GPT teacher head0.520
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

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 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

Citations1
Published2023
Admission routes4
Has abstractyes

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