Bibliographic record
Abstract
Queen’s University is a comprehensive, research-intensive, but highly decentralized institution located in Kingston, Ontario, Canada. As part of a new institutional paradigm embracing the broader, proactive principles of academic integrity, a new university role was created, known as the Academic Integrity (AI) Advisor to the Vice-Principal (Academic). Focusing on three key areas – awareness, education, and policy and procedures – the Advisor has broad responsibility for AI policy development, information gathering and sharing, and for promotion of the values of academic integrity. Free from the challenges of handling specific cases, the AI Advisor can focus on establishing best-practices in the three key areas, by drawing on the research, experiences, and analysis of other institutional practices from the Canadian and international environments. Numerous university-wide initiatives targeted at students, instructors and faculty members, and administrators, have brought together a variety of institutional partners to raise the profile of AI across the university. By building on a principle of broad institutional inclusion, this position thus provides a dynamic lens through which a variety of academic-integrity issues faced within and by universities, both centralized and decentralized, can be discussed and effectively addressed.
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 | 0.023 | 0.058 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.017 | 0.007 |
| Scholarly communication | 0.017 | 0.006 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.036 | 0.014 |
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".