Teaching the What, Why, and How of Academic Integrity: Naturalistic Evidence From College Classrooms
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
To refrain from cheating, students need to adopt an array of discipline-specific standards of academic integrity. The high rates of cheating in college show evidence that many undergraduates fall short of these standards. Little research has examined how instructors teach academic integrity, leaving gaps in our knowledge about how academic integrity develops. To examine how instructors teach academic integrity, this article reports on two studies of college courses. Researchers attended lectures and collected course materials for classes in the social sciences (N = 56, Study 1) and engineering (N = 5, Study 2) and coded all content for discussions of cheating. Instructors rarely discussed or defined academic integrity. Explanations of why students should avoid cheating were infrequent and typically referenced punishment. Consequently, many students misremembered class academic integrity policies. This research suggests that many students do not receive the instruction needed to learn academic integrity.
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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.000 | 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