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Record W3210834296 · doi:10.7202/1083332ar

Intranational University Student Mobility: A Case Study of Student Migration and Graduate Retention in Eastern Canada

2021· article· en· W3210834296 on OpenAlexaffvenueabout
Dale Kirby

Bibliographic record

VenueCanadian Journal of Educational Administration and Policy · 2021
Typearticle
Languageen
FieldHealth Professions
TopicGlobal Health Workforce Issues
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsGraduation (instrument)PopulationGeographyDemographyCohortDemographic economicsPolitical sciencePsychologySociologyMedicineEconomicsEngineering

Abstract

fetched live from OpenAlex

While international student mobility has received much examination, intranational student mobility is a lesser-studied area. Data shows that residents of the four Easternmost Canadian provinces are more likely to travel outside of their home province to undertake university studies than other Canadians. Beginning in the mid-1990s, Memorial University of Newfoundland experienced a near ten-fold increase in the enrolment of students from the three nearby Maritime provinces. Previous study of this enrolment trend indicated that the increase was partially driven by Memorial’s lower tuition fees. Guided by the conceptual lenses of student choice frameworks, tuition price sensitivity analyses, and student migration studies, this study was carried out to examine the persistence and graduation rates of the 2010 Maritime student cohort, where they resided following their university studies, and factors influencing their decisions to stay or leave Newfoundland and Labrador. This research primarily relied on university administrative records and participant survey responses. The results showed that almost 40% of the 2010 Maritime student cohort had dropped out two years after their initial enrolment at Memorial and by the sixth year, their graduation rate (45%) was far below the overall graduation rate for Canadian students in undergraduate degree programs (74%). In addition, almost 78% of those who were successfully surveyed in autumn 2020 were no longer residing in Newfoundland and Labrador. While there are limitations to the interpretation of the results, they raise important questions about tuition fee polices and their connection (or not) to population growth.

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)0.000
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 score0.170
Threshold uncertainty score0.982

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 integrity0.0000.000
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 head0.083
GPT teacher head0.441
Teacher spread0.358 · 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

Citations2
Published2021
Admission routes3
Has abstractyes

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