Penalties for Academic Dishonesty in a Greek Dental School Environment
Bibliographic record
Abstract
The aim of this study was to investigate the opinions of the faculty and students of the University of Athens Dental School in Greece regarding the appropriate penalty for specific academic offenses. In addition, faculty and student opinions were compared. A questionnaire was distributed to officially registered seniors and full-time faculty members, and 177 individuals responded anonymously and voluntarily. The respondents were asked to select one from a set of nine penalties for each of fifteen hypothetical academic offenses and three cases with extenuating circumstances. Non-parametric Mann-Whitney U tests and a Wilcoxon signed-rank test, depending on the nature of variables, were used to detect significant differences in penalty scores between faculty and students. A p-value of <0.05 was considered statistically significant. The penalty scores for the fifteen offenses ranged from a mean of 2.23±1.55 to 7.25±2.64. Faculty respondents gave more severe penalties than students did for all offenses, and the finding was statistically significant (p<0.05) for eleven of the fifteen offenses. Where extenuating circumstances were added, the penalty selection altered in two of the three cases. A significantly more lenient penalty was selected by both faculty and students in these two cases. The results of this study suggest that faculty members are harsher than students for the same offenses and that extenuating circumstances can sometimes significantly change recommended penalties.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".