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Record W4312090965 · doi:10.32674/jis.v13i3.5019

International Students at Canadian Community Colleges

2022· article· en· W4312090965 on OpenAlexaffabout
Oleg Legusov, Hayfa Jafar, Olivier Bégin‐Caouette

Bibliographic record

VenueJournal of International Students · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicHigher Education Governance and Development
Canadian institutionsUniversité de MontréalSeneca Polytechnic
Fundersnot available
KeywordsHigher educationInternational educationInternationalizationImmigrationStudy abroadSocioeconomic statusPolitical scienceEconomic growthInternationalization of Higher EducationSociologyPedagogyPopulation

Abstract

fetched live from OpenAlex

This study uses Knight’s (2004) framework of internationalization in higher education to examine emerging trends involving international students at Canadian public community colleges. The findings show that the provinces’ socioeconomic and cultural differences and immigration policies have had a substantial effect on the number of international students on college campuses. The study identified seven important emerging trends within the sector. The findings suggest that an evolving approach to immigrant selection may be contributing to the reconfiguration of the colleges’ international student body, which has gone from predominantly Chinese to primarily Indian. Furthermore, the study explores new approaches used to increase the market share of international students, such as campuses exclusively for international students and partnerships with private career colleges. It also provides an analysis of the latest development, namely the shift to online instruction in response to the COVID-19 pandemic. The study highlights the importance of understanding these trends.

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)0.003
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.964
Threshold uncertainty score0.262

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.005
Science and technology studies0.0140.002
Scholarly communication0.0050.001
Open science0.0020.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0210.001

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 head0.024
GPT teacher head0.381
Teacher spread0.357 · 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

Citations5
Published2022
Admission routes2
Has abstractyes

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