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Record W3131869819 · doi:10.47678/cjhe.v50i4.188861

Discursive Power and the Internationalization of Universities in British Columbia and Ontario

2021· article· en· W3131869819 on OpenAlexaffvenueabout
Conrad King

Bibliographic record

VenueCanadian Journal of Higher Education · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicHigher Education Governance and Development
Canadian institutionsKwantlen Polytechnic University
Fundersnot available
KeywordsInternationalizationGovernment (linguistics)Context (archaeology)Higher educationPower (physics)Political sciencePacePublic relationsSociologyPublic administrationDiscourse analysisInternationalization of Higher EducationEconomic growthBusinessEconomicsInternational trade

Abstract

fetched live from OpenAlex

Universities rationalize internationalization according to paradigms that emerge from different contexts. With the advent of internationalization strategies by federal and provincial governments, what effect do government ideas have on Canadian universities? This article evaluates the discursive power of government, and its role in discourse communities pertaining to higher education internationalization. Employing a discursive institutionalist framework and qualitative research design, I evaluated discursive content at 16 Tier 1 and 2 universities in British Columbia and Ontario. The findings indicate that governments have had weak ideational influence over the past decade, especially at universities with a global or nationalorientation. Many of these universities have been undergoing a subtle shift in their internationalization rationales—although not all, and not at the same pace. Yet some Canadian universities have increasingly “looked within” to rationalize internationalization, because their discourse communities are dominated by internal voices more concerned with organizational context than global competitiveness.

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 score0.185
Threshold uncertainty score0.995

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)0.0010.000

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 head0.006
GPT teacher head0.242
Teacher spread0.237 · 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
Published2021
Admission routes3
Has abstractyes

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