Student Integrity Violations in the Academy: More Than a Decade of Growing Complexity and Concern
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Academic misconduct in Canada is a growing and complex concern, worthy of increased attention and concerted action. Yet, the press appears to be more actively engaged (at least more vocal) in raising concerns about integrity violations than many in our post-secondary institutions. This chapter presents a synopsis of the seminal work by Christensen Hughes and McCabe (in the Canadian Journal of Higher Education 36: 1–21, 2006), followed by an exploration of its treatment by the press—in particular MacLean’s magazine—following its release. We also present select stories of student misconduct as reported by the Canadian press from 2010 to 2020. From a review of these contributions, we suggest that misconduct in the academy appears to be growing in complexity, severity and by the variety of third-party stakeholders involved. Types of cheating identified in this review include: the use of wearable, wireless high-tech devices for communicating with accomplices; paying (bribing) TAs for answers and inflated grades; exam impersonation; plagiarism; and contract cheating (customized essay buying from freelance writers and essay sweatshops). Explanations provided in the press for these behaviours, include increasing numbers of international students, the proliferation of contract cheating services, and increased use of on-line assessment, resulting from the Covid-19 pandemic. The chapter concludes with a call to action, for all post-secondary institutions, to a greater commitment to academic integrity, including stepping up efforts to educate faculty and students as well as to embrace innovation in assessment design and invigilation practice. We also suggest advocacy for introducing laws that will help to deter contract cheating services.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.023 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it