Perceptions of Canadian Dental Faculty and Students About Appropriate Penalties for Academic Dishonesty
Bibliographic record
Abstract
The purpose of this investigation was to a) compare the opinions of Canadian faculty and students as regards to what they felt was an appropriate penalty for particular academic offenses and b) to analyze the results and create a jurisprudence grid to serve as a guideline for appropriate disciplinary action. Two hundred questionnaires were distributed to the ten dental colleges in Canada. Each college was asked to have ten faculty and ten students complete the survey. A response rate of 100 percent was achieved for students and 92 percent for faculty. The questionnaire required respondents to select what they felt were appropriate penalties for a list of fifteen academic offenses and to render judgment on three specific cases. Statistical analysis of survey responses led to the following conclusions: 1) students gave equal or more lenient penalties than faculty for the same offense; 2) extenuating circumstances introduced via case presentations altered penalty choice only slightly; and 3) offenses could be grouped to correspond with appropriate penalties, thereby establishing a jurisprudence grid that may serve as a guideline for adjudication committees.
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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 | 0.007 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.009 | 0.007 |
| Scholarly communication | 0.006 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".