Bibliographic record
Abstract
Quality assurance and academic integrity are intertwined, and you cannot have one without the other. Academic integrity (AI) issues are a constant threat to the quality of Canadian academic programs, degrees, and educational institutions, and it has been an on-going challenge to maintain and promote AI in higher education. However, it is rarely acknowledged that AI is embedded in our national degree standards. Therefore, Canadian universities can and should leverage this connection in their academic program reviews (i.e., quality assurance process). This will lead to better support for AI initiatives and it will hold programs accountable for efforts in this area. Moreover, embedding academic integrity in academic program reviews ensures that AI will be regularly examined approximately every 8 years. It also reinforces the importance of AI, and helps protect the credibility of academia. Breaches of integrity chip away at the foundation of academia and it puts the credibility of higher education at risk. In particular, here are two main concerns involving students: 1) the potential for students to graduate without having the required degree competencies, and 2) the possibility that students who engaged in academic misconduct in school might engage in this behaviour in their career. From a job readiness perspective, students who have not earned or demonstrated their degree qualifications will not be prepared to contribute to their field; moreover, they can also pose a danger to others (IIEP-UNESCO, 2016; ICAI, 2016). In addition, research indicates that students who engaged in academic misconduct may be more inclined to act with misconduct in their careers (Denisova-Schmidt, 2018; IIEP-UNESCO, 2016). Maintaining integrity in higher education is key to preparing students as social and civically-responsible members of society (International Center for Academic Integrity, 2014, pg. 15). According to an advisory statement released by the International Institute for Educational Planning (IIEP) of the United Nations Educational, Scientific and Cultural Organization (UNESCO) in 2016, “corruption in higher education has a high cost to society” (p. 2), and “...quality assurance systems must take a leading role in this battle” (p. 1). Promoting and maintaining AI in higher education is a constant challenge that many institutions struggle with. However, there is a little-known fact that can assist educational institutions: academic integrity is embedded in our national degree standards under the section of Professional Capacity/Autonomy (Council of Ministers of Education, 2007). Canadian universities can and should leverage this connection in their academic program reviews (i.e., quality assurance process). Emphasis on academic integrity could be raised by ensuring that program reviews explicitly address AI. This reinforces the importance of AI and encourages programs to develop initiatives that promote academic integrity. Enhancing attention on academic integrity in all academic program reviews at universities across Canada would help solidify students, instructors, staff and administration’s understanding of AI, and its place as the foundation for academia and maintaining the quality of our degrees.
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How this classification was reachedexpand
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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; both teacher heads agree on what is shown here.
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".