Understanding Provincial and Territorial Academic Integrity Policies for Elementary and Secondary Education in Canada
Bibliographic record
Abstract
Abstract Research on academic integrity and misconduct in higher education is not difficult to locate, as work in this area has increased dramatically over the past several decades. Overall, findings reveal that cheating is a serious problem plaguing higher education with many institutions documenting various approaches to address the relevant issues. A careful look at this literature, however, exposes significant gaps in our understanding of academic integrity and misconduct in Canadian elementary and secondary (or K-12) education, which is problematic as behaviours practiced in these settings during the formative years may influence behaviours in later life stages. Furthermore, school policies, which reinforce expectations for students and teachers in the workplace are of particular importance as K-12 teachers arguably impact students’ approaches to academic integrity. This chapter focuses on key questions related to K-12 education in Canada: Do provincial and territorial ministries of education address academic integrity through policy for K-12 education? If these policies exist, what evidence demonstrates their influence on the implementation of academic integrity education at the school level? To begin to examine these questions, I conducted an environmental scan of Canadian ministries of education websites to identify academic integrity and misconduct policies. I found that only a few education ministries outline student expectations for academic integrity and consequences for misconduct or describe teacher responsibilities for providing academic integrity education and responding to academic misconduct (i.e., Newfoundland and Labrador, Manitoba, and Saskatchewan). To conclude this chapter, I discuss the implications of the presence or absence of effective academic integrity and misconduct policies for K-12 education in Canada and beyond, the impact on higher education and advanced training, as well as avenues for future research in the field.
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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 | 0.008 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.015 | 0.005 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".