Neither abuse, nor neglect: A duty of care perspective on academic integrity
Bibliographic record
Abstract
Approaches and mindsets related to academic integrity are increasingly bifurcating into two polarized camps: one that is characterized by a law-and-order approach and one that prioritizes student experience. The first has been accused of being abusive or insensitive to the stress and anxiety that the approach may cause students, the latter of being neglectful of the need to maintain high standards of academic integrity. This polarization is unhelpful as it hinders thoughtful discussion as well as the formulation of balanced solutions that maintain high standards of academic integrity while also being sensitive to the psycho-emotional needs of students. To address these issues, we propose a duty-of-care perspective, which is based on the principle that as educators, we have a duty-of-care obligation to others and we must therefore act to address academic misconduct, but not without a consideration of the costs and burdens it places on others. Our duty-of-care perspective offers a framework that provides (1) a prosocial motivation and frame of reference for dealing with academic integrity, (2) a guide for developing and assessing alternative courses of action in a balanced and holistic way and, (3) a frame for messaging to stakeholders that we have a duty to act based upon care and shared responsibilities. If we are all in this together, rather than retreating into opposing camps, the duty-of-care perspective unites us around our shared responsibilities.
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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 | 0.025 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.048 | 0.186 |
| Scholarly communication | 0.024 | 0.020 |
| Open science | 0.005 | 0.019 |
| Research integrity | 0.017 | 0.026 |
| 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".