The Barriers to Faculty Reporting Incidences of Academic Misconduct at Community Colleges
Bibliographic record
Abstract
Abstract Academic misconduct is a growing concern within Canadian higher education and around the world. Research suggests that university faculty have an extensive history of addressing academic misconduct, with an increased focus on detection and prevention. There has been little research, however, on faculty teaching in community colleges and their experiences with reporting and prevention, particularly within the Canadian context. As concern with academic misconduct continues to rise, we suggest that there needs to be more focus on these issues, particularly with respect to approaches that support a cultural shift with faculty that encompasses the fundamental values of academic integrity. For this to occur, it is essential for educational institutions to understand the forces that influence potential dishonest behaviors among students, create policies to address and support academic integrity, while creating a culture of academic integrity which supports both faculty and students alike. Faculty play a crucial role in creating environments that expound and uphold the values of academic integrity. Faculty are the frontline contact, espousing the values and expectations of their institution to students, monitoring, and reporting. Our scholarship of teaching and learning (SoTL) research was motivated by the aim to help community college faculty address the issue of academic misconduct within their classrooms and institutional environments. Barriers to reporting academic dishonesty, identified by faculty, include time and workload in reporting, a perceived lack of institutional support from administration and applicable institutional policies, as well as the perceived threat felt by faculty in reporting incidents.
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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 | 0.023 | 0.188 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.014 | 0.005 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".