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Record W1991495440 · doi:10.1080/1476772032000141807

‘Hardly Neutral Players’: Australia's role in liberalising trade in education services

2003· article· en· W1991495440 on OpenAlexaboutno aff
Christopher Ziguras, Leanne Reinke, Grant McBurnie

Bibliographic record

VenueGlobalisation Societies and Education · 2003
Typearticle
Languageen
FieldSocial Sciences
TopicGlobal Education and Multiculturalism
Canadian institutionsnot available
Fundersnot available
KeywordsLiberalizationOpposition (politics)SubsidyFree tradeGovernment (linguistics)International tradeFlaggingMarket accessEconomicsBusinessInternational economicsPolitical scienceMarket economyLawPolitics

Abstract

fetched live from OpenAlex

In this paper we explore the avowedly partisan stance of Australia regarding trade in education services, the government's enthusiastic approach to market liberalisation through GATS, and its response to the concerns of vocal critics including academic and student unions. Education is a major export enterprise for Australia, both onshore and offshore. Education is publicly referred to as an ‘industry’ as often as a ‘sector’, and institutions behave in an aggressively market‐oriented manner unthinkable two decades ago. While other major education exporters, including the EU, US and Canada, have showed flagging commitment to pursuing trade liberalisation in education due to opposition from the academic community and little pressure from the ‘industry’ for such an approach, Australia and New Zealand have come to represent the most ardent supporters of free trade in education. The Australian government's approach has involved making changes to its domestic education regulations to ensure they are consistent with market liberalisation, and facilitating the exploration of concerns through various international forums, including APEC, the WTO and OECD. Issues such as quality assurance, consumer protection, and public subsidies remain high on the list of matters for further debate.

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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.317
Threshold uncertainty score0.990

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
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 head0.022
GPT teacher head0.331
Teacher spread0.310 · 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 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

Citations14
Published2003
Admission routes1
Has abstractyes

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