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Record W4319036426 · doi:10.2399/yod.22.202202

Professors and Internationalization in Canada: Academic Disciplines and Global Activities

2022· article· en· W4319036426 on OpenAlexaffabout
Grace Karram Stephenson, Glen A. Jones, Olivier Bégin‐Caouette, Amy Scott Metcalfe

Bibliographic record

VenueYuksekogretim Dergisi · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicGlobal Education and Multiculturalism
Canadian institutionsUniversity of British ColumbiaUniversité de MontréalUniversity of Toronto
Fundersnot available
KeywordsInternationalizationDisciplineCurriculumDecentralizationHigher educationCorporate governanceSociologyPolitical scienceThe artsPedagogyPublic relationsEngineering ethicsSocial scienceManagementEngineeringBusiness

Abstract

fetched live from OpenAlex

This paper examines professors’ perceptions of internationalization activities at Canadian universities using the findings of the Academic Profession in the Knowledge-based Society (APIKS) survey. The findings suggest academic disciplines are the organizing logic for diverse manifestations of internationalization within the same universities. Professors in the hard sciences are more likely to publish internationally while those in the arts and humanities are more likely to internationalize their curriculum. The findings are analysed contextually, pointing to the decentralization of Canadian higher education as well as university governance which has exacerbated, yet rarely recognized, these disciplinary divides. The paper calls for new conceptual understandings of internationalization that take into account disciplinary divides.

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)0.002
metaresearch head score (Gemma)0.009
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 score0.950
Threshold uncertainty score0.361

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.007
Science and technology studies0.0110.006
Scholarly communication0.0060.002
Open science0.0010.004
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0070.000

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 head0.025
GPT teacher head0.353
Teacher spread0.328 · 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

Citations1
Published2022
Admission routes2
Has abstractyes

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