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Record W3128343755 · doi:10.51357/jei.v2i1.134

Internationalization in Ontario Colleges

2021· article· en· W3128343755 on OpenAlexaffabout
Adam McGregor, Bill Hunter

Bibliographic record

VenueJournal of Educational Informatics · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicHigher Education Governance and Development
Canadian institutionsOntario Tech UniversitySt. Lawrence College
Fundersnot available
KeywordsInternationalizationMandateEconomic shortageBusinessHigher educationPolitical scienceInternationalization of Higher EducationPublic administrationEconomic growthPublic relationsGovernment (linguistics)EconomicsInternational trade

Abstract

fetched live from OpenAlex

Internationalization of Ontario colleges is a strategic mandate from both the federal and provincial governments to address declining domestic enrollment, labour market shortages, and the evolving needs of 21st century post-secondary students. The growth in numbers of international students in Ontario colleges has been particularly rapid over the past five years, and existing literature on internationalization and Ontario colleges was limited. Therefore, a careful review and analysis of policy at both the federal and provincial level can help Ontario colleges understand what policy has done to create the current environment for internationalization, anticipate how policy will impact the future of internationalization, and support decision making as colleges try to find success in this continuously changing landscape. The results of this review and analysis of policy surrounding internationalization in the Ontario College system indicate a probable need for additional research, funding, training, and policy changes to ensure a sustainable future. Keywords: internationalization, international students, Ontario colleges, higher education policy

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)0.003
metaresearch head score (Gemma)0.007
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.893
Threshold uncertainty score0.774

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.006
Science and technology studies0.0050.005
Scholarly communication0.0050.002
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.022
GPT teacher head0.331
Teacher spread0.309 · 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

Citations0
Published2021
Admission routes2
Has abstractyes

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