Down the Slippery Slope: Moral Disengagement and Academic Integrity’s Grey Areas
Bibliographic record
Abstract
Most students do not engage in serious cheating, but many engage in seemingly insignificant transgressions. While these small misgivings may be brushed aside as trivial, prior research shows that major ethical violations tend to follow small, common violations – the so-called slippery slope effect. In this study we combine computer-facilitated focus groups, an online survey and open-ended responses to identify when and why students think it is acceptable to engage in both specific minor academic integrity violations and violations more generally. We also demonstrate that the slippery slope effect occurs in academia as students who find it acceptable to violate academic integrity in more “grey area” situations also engage in more trivial and non-trivial academic integrity violations in general. We analyze our findings using mechanisms of moral disengagement and neutralization theory to categorize why students violate academic integrity and find that the mechanisms they use to justify engaging in specific trivial violations differ from those they use to justify violating academic integrity more generally. We conclude with recommendations that directly address ways that faculty and administration can neutralize the mechanisms that students use to morally disengage both in relation to specific trivial violations as well as more generally.
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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 | 0.011 | 0.065 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.012 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 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".