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Record W4392774448 · doi:10.1177/00207020241232989

Accidental paradiplomats? The curious case of Ontario school board budgets and Canadian soft power projection

2024· article· en· W4392774448 on OpenAlexafffundabout
Michael P. A. Murphy

Bibliographic record

VenueInternational Journal Canada s Journal of Global Policy Analysis · 2024
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicTaxation and Legal Issues
Canadian institutionsQueen's University
FundersSocial Sciences and Humanities Research Council of CanadaMitacsGovernment of Canada
KeywordsAccidentalSoft powerPower (physics)Projection (relational algebra)Political scienceLawComputer sciencePoliticsPhysics

Abstract

fetched live from OpenAlex

From the earliest studies of soft power in International Relations, the importance of educational exchanges has been well-established. Studies of international education in the context of Canadian soft power often draw on cases from the higher education sector. This article argues that greater attention should be paid to the K-12 level, especially as budgetary pressures in Ontario's education system are leading school boards to rapidly expand their international student recruitment efforts. Although this is not an example of intentional soft power projection, it nevertheless represents an important reminder that subnational actors may accidentally become paradiplomats whose actions have consequences on the international level. Further, this case reveals the importance of paying attention to actors typically overlooked by IR scholarship. Drawing on Joseph Nye's theory of soft power and in conversation with prior research on international education as a mechanism of soft power projection, this article traces the thread between budgetary pressures in Ontario school boards and the broader context of soft power projection.

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)0.003
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.853
Threshold uncertainty score0.989

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.004
Science and technology studies0.0380.026
Scholarly communication0.0100.004
Open science0.0020.006
Research integrity0.0030.004
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 head0.007
GPT teacher head0.268
Teacher spread0.261 · 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 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

Citations3
Published2024
Admission routes3
Has abstractyes

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