Managing Academic Integrity in Canadian Engineering Schools
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.004 | 0.069 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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 it