Bibliographic record
Abstract
Abstract This chapter explores what engineering schools across Canada are doing to address and advance academic integrity amongst their students, including how they are currently promoting academic integrity and managing related academic misconduct issues. Responses from a national survey are compared to identify the approaches and practices that are more widely adopted, as well as unique approaches that may warrant broader use. Input was also received from the twelve provincial and territorial engineering regulators that operate across the country. In addition to identifying areas of success, potential opportunities for additional progress are identified. This work serves as a starting point for dialogue among universities and regulators. All parties have a vested interest in strengthening the integrity of engineering students during their academic training and professional development. It is clear from this study that a collective effort is needed to develop solutions, educate faculty, and mentor students to achieve a higher standard of academic integrity. The successes and opportunities highlighted here may be helpful to other professional programs, such as nursing, medical, dentistry, law, and business schools, where integrity is also of extreme importance.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.024 | 0.007 |
| Scholarly communication | 0.011 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".