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.
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.
How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.010 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".