Championing academic integrity in academic development
Bibliographic record
Abstract
Curtis et al. (2021) propose that educators with practical, theoretical, and research experience in academic integrity (AI) are well-suited to deliver workshops on the subject. These workshops promote shared understandings amongst attendees, and provide a platform to discuss concerns, devise solutions, and relieve anxieties. Finally, these workshops are most effective when they are a part of themed academic development activities. Assiniboine Community College’s (ACC) Centre for Learning and Innovation (CLI) supports program development and renewal, course and instructional design, teaching strategies, Moodle (Learning Management System), and educational technology. Working with the College’s Academic Integrity and Copyright Officer, CLI has contextualized academic integrity within existing academic development activities, such as a workshops, job aids, and one-on-one sessions. This situates academic integrity as central to our work, rather than an add-on topic. Join ACC’s Centre for Learning and Innovation team members for an overview of where and how we have embedded academic integrity into our offerings, work, and quality standards. Participants will leave this session with practical examples of how teaching and learning centres can be champions for academic integrity.
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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 | 0.041 | 0.064 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.021 | 0.054 |
| Scholarly communication | 0.026 | 0.014 |
| Open science | 0.003 | 0.026 |
| Research integrity | 0.009 | 0.013 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".