It's Not Robotic: Barriers to Navigating Academic Integrity & the Role of Emotions
Bibliographic record
Abstract
In December, 2022, Conestoga College’s Academic Integrity Office launched an all-faculty survey on Academic Integrity. It was sent to just under 2,500 full-time and part-time faculty, and we received 989 faculty responses, or a 40% response rate. Focus Groups were also conducted to enrich our findings with qualitative data. Generally, this survey is being conducted to better understand how faculty interact with the institution’s academic integrity policies and procedures and to identify faculty interpretation, and possible improvements to supports and resources. In particular, we are keen to ascertain data on two important, yet neglected, questions: What are the barriers faculty experience when navigating academic integrity violations and do these vary depending on faculty type (Full-Time vs. Part-Time). How do discrete emotions impact faculty when navigating academic integrity violations, and do emotions impact penalty decisions? It her chapter titled, “Impediments to Reporting Contract Cheating: Exploring the Role of Emotions” in A Research Agenda for Academic Integrity (Ed. T. Bretag, 2020), Felicity Prentice states, “if emotional responses by academic staff to breaches of academic integrity affect judgement and decision-making, […] it is timely to address this as a potentially significant avenue for research” (p. 70). Potentially significant avenue for research, indeed. Overall, participants will glean an understanding of the barriers faculty face as well as how emotions may influence the process of navigating 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 | 0.012 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.011 |
| Scholarly communication | 0.013 | 0.008 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.007 |
| 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".