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
Cheating in academia is defined multidimensionally and might include dishonesty, fraud, stealing, and unauthorized use. This behaviour appears to be on the rise in higher education, though it may be somewhat subjective. Beyond the ethical issue of cheating, inadequately learned skills and unqualified practitioners put lives at risk (e.g., medicine, engineering), as well as the institution’s reputation and integrity in producing proficient graduates. We asked Canadian students and faculty from a two-year college to define academic cheating in their own words and rate a number of behaviours to indicate their perception of whether the behaviour should be considered cheating or not. Overall, there was a great overlap between the themes evoked in students’ and faculty’s definitions of cheating. Differences between students’ and faculty’s ages might suggest a different degree of moral reasoning which may have impacted the responses. This study further contributes to knowledge about cheating because we surveyed college students (rather than university students), which are greatly under-represented in the literature.
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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.003 | 0.012 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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