Academic Integrity Policy Analysis of Alberta and Manitoba Colleges
Bibliographic record
Abstract
Dealing with matters related to academic integrity and academic misconduct can be challenging in higher education. As a result, students, educators, administrators, and other higher education professionals look to policy and procedures to help guide them through these complex situations. Policies are often representative of an institution’s culture of academic integrity. For these and other reasons it is therefore important that policies and procedures are reviewed regularly and updated to ensure that they align with current educational expectations and societal context. In this presentation, we share the results from our policy analysis of 16 colleges in the Canadian western provinces of Alberta and Manitoba. Data extraction and analyses were performed using a tool developed based on Bretag et al.’s five core elements of exemplary academic integrity policy. Our results showed inconsistencies in college polices in terms of the intended audience for the documents (e.g., students, faculty, administrators), varying levels of detail, inconsistent definitions, or categories of misconduct (e.g., plagiarism, cheating) and little mention of contract cheating. We compare the results of this study with previous academic integrity policy research in Canada for colleges in Ontario (Stoesz et al., 2019), as well as universities (Miron et al., 2021; Stoesz & Eaton, 2022). We also discuss the recent increase in the use of artificial intelligence tools such as ChatGPT and GPT-3 and what this could mean in the context of academic integrity policy. We conclude with recommendations for policy reform in the Canadian college context. Our findings may be useful to those working in community colleges and polytechnics elsewhere.
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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 | 0.013 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.022 | 0.049 |
| Science and technology studies | 0.017 | 0.005 |
| Scholarly communication | 0.007 | 0.001 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".