Using Quality Assurance Frameworks to Support an Institutional Culture of Academic Integrity at Canadian Universities
Bibliographic record
Abstract
Abstract In Canada, there is a national academic quality assurance framework—the Canadian Degree Qualifications Framework (CDQF) that guides quality assurance standards within universities across the provinces and territories. These standards exist to support the quality and consistency of postsecondary academic programming in Canada, and provide mechanisms for quality enhancement. The CDQF is supported by further quality assurance mechanisms at the provincial level. While the CDQF includes the notion of academic integrity as a learning outcome requirement, the implementation and review of this quality indicator across the sector is nebulous. The ongoing support for a culture of academic integrity requires a holistic approach, which includes the alignment of various policies and processes. It also involves the inclusion of academic integrity best practices into quality assurance processes, such as curriculum development and program review. In this chapter we discuss several quality assurance tools used in Canadian universities, with a focus on Ontario institutions, and discuss opportunities to leverage them to support academic integrity. The CDQF and provincial/territorial quality assurance frameworks should be better utilized for a holistic response to academic misconduct, to strengthen teaching and learning, and develop a culture of integrity in higher education. Opportunities within cyclical program review, curriculum mapping and educational development are discussed to highlight opportunities for academic integrity specialists, quality assurance staff, faculty, and policy makers to raise academic integrity awareness and weave best practices across an institution. Implications for the community college sector are also included. Recommendations can be applied to postsecondary institutions across Canada and integrated with quality assurance practices promoted by the Quality Assurance Agency (QAA) and others academic integrity advocates around the world.
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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 | 0.106 | 0.142 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.032 | 0.024 |
| Scholarly communication | 0.033 | 0.009 |
| Open science | 0.007 | 0.019 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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".