Academic Integrity: Ethical approaches to teaching, learning and supporting student success during COVID-19 and beyond
Bibliographic record
Abstract
Guest lecture for the Faculty of Science, York University, Toronto, Canada. Description: In this session we address how breaches of academic integrity have changed during the COVID-19 crisis, including a look into the predatory practices of commercial contract-cheating and file-sharing industry. We will look at what educators can change and what they cannot and how to support students as they learn during the pandemic. Learning Outcomes: By the end of this session, active participants will: • Develop a better understanding of tensions between normal online behaviours and academic misconduct. • Understand the rise in predatory practices of commercial third-parties. • Generate ideas about how to teach and learn ethically in an online environment. Speaker bio: Sarah Elaine Eaton, PhD, Associate Professor, Werklund School of Education and Educational Leader in Residence, Academic Integrity specializes in research on academic integrity, including contract cheating. She is the Editor-in-Chief of the International Journal for Educational Integrity (BMC Springer Nature). In 2020 she received the Research and Scholarship Award from the Canadian Society for the Study of Higher Education for her research contributions on academic integrity in Canada.
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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 | 0.016 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.026 | 0.022 |
| Scholarly communication | 0.017 | 0.008 |
| Open science | 0.004 | 0.024 |
| Research integrity | 0.007 | 0.015 |
| Insufficient payload (model declined to judge) | 0.028 | 0.005 |
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".