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Record W2295550061

Ethics in education: Chinese learner and post-Enron ethics (editorial)

2008· article· en· W2295550061 on OpenAlexaboutno aff
Kala Saravanamuthu

Bibliographic record

VenueNOVA (University of Newcastle Australia) · 2008
Typearticle
Languageen
FieldArts and Humanities
TopicHermeneutics and Narrative Identity
Canadian institutionsnot available
Fundersnot available
KeywordsEthics of technologyEngineering ethicsInformation ethicsPolitical sciencePublic relationsPost truthApplied ethicsBusiness ethicsMeta-ethicsSociologyPedagogyLawEngineering
DOInot available

Abstract

fetched live from OpenAlex

This is the third in a series of special issues on tertiary education and academic life (Saravanamuthu and Filling, 2004; Saravanamuthu and Tinker, 2002). This issue touches on two aspects of ethical education. The first section engages with the dilemma of educating (overseas) Chinese students in universities in the UK, USA, Australia, Canada, New Zealand and parts of Western Europe: the Chinese Learner. The second part of the issue reviews ethics in education in the post-Enron environment. The genesis of Chinese Learner research is often attributed to John Biggs and David Watkins, University of Hong Kong. John has long retired, and David continues to lead the research through his doctoral students. Even though Chinese Learner studies originated in the Educational Psychology enclave, it has now permeated into other discipline areas. Theories of learning methods and techniques are inherently contestable, even before considering their cultural complications. The Chinese Learner literature touches on a number of problematic issues, beginning with the identity of this student. Any attempt to associate the Chinese Learner with a specific culture, nation, personality trait and/or learning approach immediately lends itself to criticisms of over-generalisation and stereotyping. This issue engages with the multi-layered realities of the Chinese student market, which the Corporate University (following Saravanamuthu and Tinker, 2002) plays down as it eagerly collects full-fees for its depleted coffers.

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: Empirical
Teacher disagreement score0.534
Threshold uncertainty score0.999

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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.232
GPT teacher head0.342
Teacher spread0.110 · 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

Citations0
Published2008
Admission routes1
Has abstractyes

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