MétaCan
Menu
Back to cohort
Record W4285292437 · doi:10.7202/1088300ar

Higher Education Student Migration in Canada: Interprovincial Structure and the Influence of Student Mother Tongue

2022· article· en· W4285292437 on OpenAlexaffvenueabout
Ebenezer D. Narh, Michael Buzzelli

Bibliographic record

VenueCanadian Journal of Regional Science · 2022
Typearticle
Languageen
FieldHealth Professions
TopicGlobal Health Workforce Issues
Canadian institutionsWestern University
Fundersnot available
KeywordsFirst languageHigher educationGeographyPolitical scienceSociologyDemographic economicsEconomic growthEconomicsLinguistics

Abstract

fetched live from OpenAlex

Approximately ten percent of Canadian higher education students cross provincial boundaries each year to attend college or university. Despite its size and impact, the geography of higher education student migration (HESM) is not well documented in Canada. This paper analyses Statistics Canada’s Postsecondary Student Information System (PSIS) for the academic year 2016/17. Interaction matrixes are developed and mapped to analyse both the broad geographical structure of interprovincial HESM and the impact of language, specifically student mother tongue, in shaping migration patterns. In both the overall picture of HESM and that of mother tongue, HESM generates expected patterns as well as important migration variations. Ontario and Quebec anchor the national picture and, together with others, constitute exchanges amongst contiguous provincial clusters. Unique cross-country interprovincial connections are also revealed and migration by mother tongue generates further nuances still. The paper concludes with a discussion of the implications of this work for understanding (i) migration-based integration amongst Canadian provincial higher education systems, and (ii) with further research, the multi-scalar processes and geographies of HESM for institutional and local economic policy makers.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.039
Threshold uncertainty score0.963

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.019
GPT teacher head0.367
Teacher spread0.348 · 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

Citations7
Published2022
Admission routes3
Has abstractyes

Explore more

Same venueCanadian Journal of Regional ScienceSame topicGlobal Health Workforce IssuesFrench-language works237,207