Bibliographic record
Abstract
Within the literature a lot of research has been published on academic misconduct, including why students cheat, how they cheat, and what can be done to curb the behavior. Very little research had been done to determine how schools have addressed academic integrity from a management or administrative perspective. This presentation highlights the work from a book chapter I submitted to a national project on academic integrity in Canadian post-secondary institutions. This work focused on how engineering schools and the professional engineering regulators were promoting academic integrity and dealing with academic misconduct. A survey was provided to all 43 Canadian engineering schools and the 12 provincial and territorial engineering regulators. The survey covered topics related to integrity, misconduct, professionalism, and administrative strategies and procedures. These results have been put into context with existing literature and potential best practices. This presentation will be of interest to students, instructors and administrators from all faculties. Students will learn about academic integrity and misconduct from an administrator’s perspective. Instructors will lean how to improve academic integrity in their courses. Administrators will be exposed to broader policy and practice content.
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 | 0.017 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.080 | 0.014 |
| Scholarly communication | 0.019 | 0.004 |
| Open science | 0.005 | 0.012 |
| Research integrity | 0.005 | 0.005 |
| 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".