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Record W3111811494 · doi:10.1177/0308518x20959082

The public university and the retreat from globalisation: An economic geography perspective on managing local-global tensions in international higher education

2020· article· en· W3111811494 on OpenAlexaff
Eric Knight, Andrew Jones, Meric S. Gertler

Bibliographic record

VenueEnvironment and Planning A Economy and Space · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicHigher Education Governance and Development
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsGlobalizationPerspective (graphical)UnrestFace (sociological concept)Momentum (technical analysis)Political scienceHigher educationEconomic globalizationEconomic growthEconomic geographyPolitical economySociologyGeographySocial scienceEconomicsPolitics

Abstract

fetched live from OpenAlex

As a growing backlash against globalisation gathers momentum, internationally oriented public universities face a period of great unrest. In particular, they find themselves caught between the narrowing local agendas of their public funders and the global outlook of their researchers and students. We suggest that an economic geography lens provides a powerful perspective for how universities might navigate these tensions. Specifically, we show how local-global tensions can be managed through strategies engaged at the city and regional level. Our contribution seeks to inform current debates in the higher education sector and bring economic geography more centrally into public discourse on this topic.

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)0.010
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.026
Threshold uncertainty score0.076

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0080.045
Scholarly communication0.0260.017
Open science0.0010.013
Research integrity0.0060.005
Insufficient payload (model declined to judge)0.0060.000

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 head0.014
GPT teacher head0.241
Teacher spread0.228 · 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 designQualitative
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

Citations9
Published2020
Admission routes1
Has abstractyes

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