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Record W2182126672

Neoliberalism and Public University Agendas: Tensions along the Global/Local Divide

2015· article· en· W2182126672 on OpenAlexaboutno aff
Peter Wanyenya, Donna Lester-Smith

Bibliographic record

VenueDigital Commons - URI (University of Rhode Island) · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicHigher Education Practises and Engagement
Canadian institutionsnot available
Fundersnot available
KeywordsNeoliberalism (international relations)Political scienceContext (archaeology)SociologyEconomic growthIndigenousEconomic JusticeFace (sociological concept)ConstructivePublic relationsPublic administrationPolitical economySocial scienceLaw
DOInot available

Abstract

fetched live from OpenAlex

Over the last decade, internationalization efforts have accelerated at leading postsecondary institutions in North America and elsewhere, with universities now aggressively competing for the most talented students worldwide. With the focus on recruiting international students, one of the major attendant objectives has seemingly been a social-justice-oriented agenda on tackling pressing global issues; the local has indeed become the global. However, not everyone is ostensibly benefiting from this new global focus. For some, their local issues and conditions are increasingly precarious and nonprioritized in institutional and broadening neoliberal governmental agendas. In the Canadian context, various Indigenous and low-income racialized communities, youth in particular, face multiple implications of this reality. For these communities, secondary school completion and postsecondary educational attainment are decreasing, while incarceration rates among the youth are significantly increasing. In this viewpoint paper, we briefly highlight two localized program examples, sharing our experiences as educators, and call for a constructive dialogue regarding how universities’ social justice agendas can better work for all people, both locally and globally.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.594
Threshold uncertainty score0.989

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 studies0.0010.001
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.075
GPT teacher head0.279
Teacher spread0.204 · 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 designNot applicable
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
Published2015
Admission routes1
Has abstractyes

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