Academic Integrity in Canada: Historical Perspectives and Current Trends
Bibliographic record
Abstract
Abstract In this chapter we discuss the development of academic integrity in Canada. We begin by offering insights into how provincial and territorial educational governance and policy structures have affected academic integrity in Canada, compared to other countries, such as the United States. In particular, we discuss why it may not make sense for Canadian schools to try to adopt the American honour code model. We explore the evolution of higher education in Canada, highlighting the earliest incidents of academic misconduct on record as well as the development of academic integrity scholarship, focusing on significant contributions and its impact over time. In particular, we draw attention to the emergence of policies, practices, associations, and networks intended to help Canada’s higher educational institutions develop and strengthen cultures of integrity. Following, we discuss how the academic integrity landscape has shifted, noting recent trends such as the rise of contract cheating. We conclude with a call to action for more enhanced support for academic integrity scholarship to support advocacy, policy, and practice.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.009 | 0.026 |
| Science and technology studies | 0.023 | 0.017 |
| Scholarly communication | 0.019 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.010 | 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".