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Record W3031623892 · doi:10.4995/head20.2020.10820

The Impact of Government Policy on Higher Education International Student Recruiters

2020· article· en· W3031623892 on OpenAlexaffabout
Melissa James

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldSocial Sciences
TopicHigher Education Governance and Development
Canadian institutionsUniversity of Prince Edward Island
Fundersnot available
KeywordsGovernment (linguistics)InternationalizationInternationalization of Higher EducationPerceptionPublic relationsPublic policyHigher educationBusinessPolitical scienceEconomic growthPsychologyEconomicsInternational trade

Abstract

fetched live from OpenAlex

This paper explores higher education actors involved in the recruitment of internationalstudents and their perceptions of their home country’s government policy on their practice. It examines case study institutions from three countries Canada, Hong Kong, and the United Kingdom. This study shows higher education institutions do not exist in a vacuum and regardless of their location, government policy shapes perceptions for international student recruiters who believe that government policies contribute or hinder their practice. All of the participants, regardless of location, show a high level of awareness of government policy that greatly shapes their strategies. More specifically, recruiters find tensions arising from these policies with government shaping recruitment priorities and restricting or instigating competitive responses, while their institutions do not challenge government policy (enough). The findings suggest that government policies establish the “playing field” for recruiters as they attempt to navigate an increasingly competitive environment but at the same time, these perceptions are highly localized and need to be understood in their individual settings.Keywords:internationalization; government policies; recruiters; students

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.733
Threshold uncertainty score1.000

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 head0.039
GPT teacher head0.422
Teacher spread0.383 · 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.

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

Citations1
Published2020
Admission routes2
Has abstractyes

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