Bibliographic record
Abstract
Preface Acknowledgments Contributors Table of Cases Table of Statutes Introduction - John Goldring Australia Stuart Clark and Jocelyn Kellam Cambodia Ly Tayseng Canada Susan Paul and Peter J Cavanagh China Weining Zou and Xiaochun Wang Fiji Rodon King Hong Kong Allan CY Leung India Karnika Seth Indonesia Duane J Gingerich Japan Luke Nottage and Hiroyuki Kano Republic of Korea Eui Jae Kim and Hyung-Joon Park Macau Bruno Nunes Malaysia Lim Chee Wee and Ravneet Kaur Gill Myanmar Thida Aye and James Finch New Zealand Robert Gapes Papua New Guinea Erik Andersen Philippines Hector M de Leon, Jr Singapore Lawrence Teh Sri Lanka John Wilson Taiwan Matt Liu, CY Huang Thailand Alastair Henderson and Surapol Srangsomwong United States Kenneth Ross and Professor J David Prince US-Affiliated Pacific Island Jurisdictions Craig Miller Vanuatu John Ridgway Vietnam John E King and Tu Ngoc Trinh Product Liability Insurance Kemsley Brennan and Chris Lees Conclusion Trends Towards Strict Liability and Consumer Product Safety Regulation David Harland and Luke Nottage Contact details for contributors Index
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.093 | 0.022 |
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".