Bibliographic record
Abstract
Academic integrity is valued in all Canadian educational systems, yet no real accounting of academic integrity violations (AIVs) exists primarily because faculty under-report them. Numerous disincentives dissuade faculty from reporting AIVs, and voluntarily reporting violations increases emotional labour. Still, some faculty feel duty-bound to do so. This paper explores the neglected emotional experience when reporting AIVs using a phenomenological approach. Interviews with a purposive, homogenous sample of faculty at a small Canadian community college who reported AIVs reveal that reporting AIVs disturbed relationships with students, and that navigating bureaucratic processes, when other faculty choose not to, caused frustration. After reporting, faculty in this study felt alienated from the outcomes of their decisions. Still, they remained committed to reporting AIVs because it was part of their self-definition as educators to defend the innocent and protect the future. This small sample of faculty identify personal experiences and institutional barriers that may discourage faculty from reporting AIVs. Finally, the findings reveal a gap between faculty and international students’ understanding of academic integrity. Bridging this gap is important because of the intensified emotional and relational challenges arising from the more serious consequences of reporting AIVs involving international students. The findings reveal a need for faculty development opportunities that build intercultural competence and handle AIVs in a way that respects diverse worldviews and promotes the values of 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 | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.020 | 0.028 |
| Scholarly communication | 0.015 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".