Academic Integrity and Mental Well-being: Exploring an Unexplored Relationship
Bibliographic record
Abstract
The rapid and accelerated shift to online learning during the COVID-19 pandemic has heightened parallel conversations about student well-being and academic integrity in higher education. On one hand, post-secondary students have been under increased pressure to succeed in stressful learning and societal environments. On the other hand, reports of student academic misconduct have increased throughout the COVID-19 pandemic. There is an urgent need to consider the intersecting relationship between mental well-being and academic integrity to foster supportive, learner-focused, and caring higher education environments. In this session, we will open a conversation about this widely unexplored relationship. We will present the findings of a rapid review wherein we investigated how the academic integrity literature had taken up mental well-being. We will address ways that student well-being should be considering in academic integrity research and practice, such as the need to care for student well-being during academic misconduct incidents. Participants will leave this session with lessons that will be applicable during the ongoing COVID-19 pandemic and beyond.
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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 | 0.014 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.005 | 0.013 |
| Scholarly communication | 0.012 | 0.010 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".