Bibliographic record
Abstract
Why is academic integrity so important? This might seem like a frivolous question, but it really is not. Academic integrity is crucial if we consider that one of the prime missions of higher education is to help form the intellectual and moral outlook of the future leaders of our society. By so doing, higher education can contribute to societies whose members abide by the rule of law and maintain, for the most part, adherence to a shared legal and moral code. Maintaining the ethical standards of the academy is also crucial to maintaining public trust in our educational institutions but this imperative pales in importance to education’s role in helping to form ethical citizens. Without a shared ethical base, societies can easily slide into rampant corruption and chaos. While there has been significant work done on theoretical frameworks for promoting ethics in higher education, the vast majority of research on academic integrity actually focuses on student motivation to commit academic misconduct and how instructors and institutions can control, or limit this behavior. Current research indicates that this focus on student behavior has not worked. This presentation will present a framework for operationalizing integrity for life on a systems level with research-based guidelines for enhancing individual, institutional, education system and, ultimately, societal integrity while contributing to the development of a more holistic view of academic ethics that will be applicable to the Canadian context and beyond. Participants will take away insights to creating a roadmap to academic integrity in their own institutions and communities. Keywords: Academic integrity; ethics, framework, academic dishonesty, Canada
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.028 | 0.018 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.030 | 0.134 |
| Scholarly communication | 0.026 | 0.014 |
| Open science | 0.005 | 0.013 |
| Research integrity | 0.009 | 0.012 |
| Insufficient payload (model declined to judge) | 0.007 | 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".