Welcome Message The organising committee of the 4th Asia Pacific Conference on Educational Integrity warmly welcomes you to the University of Wollongong, New South Wales,
Bibliographic record
Abstract
Australia for this conference. To those of you who have participated in previous Asia Pacific Conferences on Educational Integrity, or who have connected through the International Journal for Educational Integrity, this is an opportunity to continue the conversation. To those who are newer to this exciting interdisciplinary field, we look forward to your critical insights and interventions over the coming days … and beyond. Many of the participants are local, but we are also welcoming people from the United States, Singapore, New Zealand and Canada. Educational integrity is always topical, highly-charged, of-the-moment, equal parts affective and intellectual, whether the issue being debated is the place of school league tables, the recruitment of international students, the role of the teacher in the ‘Googlised ’ world, the moral remit of the university, the temptation to plagiarise and cheat, or the ancient and pervasive practice of academic ‘patronage’. We hope that many of the issues brought to the conference sessions will be hotly debated and potential solutions avidly discussed.
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 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.010 | 0.018 |
| Insufficient payload (model declined to judge) | 0.108 | 0.055 |
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".