Building a Regional Academic Integrity Network: Profiling the Growth and Action of the Academic Integrity Council of Ontario
Bibliographic record
Abstract
Since 2008, the Academic Integrity Council of Ontario (AICO) has provided a forum for academic integrity practitioners and representatives from post-secondary institutions in Ontario to share information, and to facilitate the establishment and promotion of academic integrity best practices. This article is a summary of a presentation given at the Canadian Symposium on Academic Integrity, which was organized and held at the University of Calgary in April 2019. The creation, operation and role of the Council, and how it serves to connect post-secondary institutions in Ontario on academic integrity-related matters is described. This will include the benefits that such an association brings between institutions, and some of the accomplishments to date, like the establishment and ongoing work of a contract cheating sub-committee. The most recent achievement of mobilizing academic integrity practitioners at a national level is also described.
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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 | 0.012 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.024 | 0.009 |
| Scholarly communication | 0.012 | 0.004 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".