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Record W2052512196 · doi:10.1108/13555851211278079

Revisiting the global market for higher education

2012· article· en· W2052512196 on OpenAlexaboutno aff
Tim Mazzarol, Geoffrey N. Soutar

Bibliographic record

VenueAsia Pacific Journal of Marketing and Logistics · 2012
Typearticle
Languageen
FieldSocial Sciences
TopicHigher Education Governance and Development
Canadian institutionsnot available
Fundersnot available
KeywordsCompetition (biology)ChinaDestinationsOriginalityHigher educationInternational educationState (computer science)Value (mathematics)Political sciencePrincipal (computer security)Economic growthMarketingEconomicsTourismBusiness

Abstract

fetched live from OpenAlex

Purpose The purpose of this paper is to review the changes in the international education sector that have taken place over the decade since the authors' book, The Global Market for Higher Education was published in 2001. Design/methodology/approach The paper is an expert opinion that draws on global trends in the international education sector. Findings Since the publication of the authors' book, the global market for higher education has changed significantly. A decade ago competition was between a few mainly English language instruction countries in the developed world. The principal destination country was the United States followed by Britain, but with Australia, Canada and New Zealand actively competing. In 2012, competition has expanded, with former sending nations (e.g. Singapore, China, India) becoming destinations. Competition among established nations has also intensified. Originality/value This paper provides a strategic overview of the state of international education and a unique perspective on the trends that have shaped and will continue to shape this industry into the future.

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)0.007
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.015
Threshold uncertainty score0.055

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.006
Science and technology studies0.0020.010
Scholarly communication0.0150.015
Open science0.0010.004
Research integrity0.0040.005
Insufficient payload (model declined to judge)0.0140.001

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 head0.028
GPT teacher head0.331
Teacher spread0.303 · 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 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

Citations117
Published2012
Admission routes1
Has abstractyes

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