Bibliographic record
Abstract
Research suggests that the majority of U.S. undergraduate students have engaged in some form of misconduct while completing their academic work, despite knowing that such behaviour is ethically or morally wrong. U.S.-based studies have also identified myriad personal and institutional factors associated with academic misconduct. Implicit in some of these factors are several institutional strategies that may be implemented to support academic integrity: revisiting the values and goals of higher education, recommitting to quality in teaching and assessment practice, establishing effective policies and invigilation practices, providing educational opportunities and support for all members of the university community, and using (modified) academic honour codes. There is a dearth of similar research in Canada despite growing recognition that academic misconduct is a problem on Canadian campuses. This paper suggests that Canadian higher education can learn much from the U.S. experience and calls for both a recommitment to academic integrity and research on academic misconduct in Canadian higher education institutions.
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 | 0.045 | 0.132 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.016 | 0.007 |
| Science and technology studies | 0.022 | 0.048 |
| Scholarly communication | 0.024 | 0.031 |
| Open science | 0.005 | 0.013 |
| Research integrity | 0.011 | 0.010 |
| Insufficient payload (model declined to judge) | 0.005 | 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".