Ethics in education: Chinese learner and post-Enron ethics (editorial)
Bibliographic record
Abstract
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 machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.011 | 0.010 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".