A model for preventing academic misconduct: evidence from a large-scale intervention
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 It is well known that students intentionally and unintentionally commit academic misconduct, but how can universities prevent academic misconduct and foster a culture of academic integrity? Based on a literature synthesis, an actionable Model for Preventing Academic Misconduct is presented. The model’s basic premise is that students’ voluntary participation in individual courses or academic integrity modules will have far less impact on preventing academic misconduct than required faculty or university-wide programming in core courses. In validating the model, the steps taken by the School of Business at a Canadian university to prevent academic misconduct are examined. Two online tutorials were created and implemented as required modules in the School of Business introductory core courses. Actual academic misconduct incidents recorded by the University from 2016 to 2021, a three-year pre-intervention period and a two-year post-intervention period partly covering the COVID-19 outbreak, are used to gauge the model’s effectiveness in preventing academic misconduct. The findings are discussed through a Social Learning Theory lens: the high-level implementation gives rise to a culture of academic integrity propelled by the establishment of common knowledge.
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 | 0.006 | 0.010 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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