Bibliographic record
Abstract
Academic misconduct amongst students is a consistent problem in education.Plagiarism, cheating in exams and the dangers of inappropriate online comments or behaviours can have significant negative effects upon the student [1].Academic misconduct is particularly important because there has been a drastic increase in cases all around the world in recent history [2].The present study took a sample of 121 students from Humber College Lakeshore Campus, Toronto, Canada.This study was interested in exploring students' knowledge of academic policy through testing; participants were split into two groups where one was exposed to written policy and the other to audio-visual policy.The students were then tested on the policy and were compared based on their results.With the exception of one question there was no statistical significance when comparing these two groups.Moreover, in what has commonly been referred to as the fool's dilemma, students who selected incorrect responses actually reported a high level of confidence in their responses.This is troubling because cases of academic dishonesty can be detrimental to academic and professional careers [2][3].It is essential that issues of academic misconduct be understood and studied at an extensive level because issues of academic misconduct can potentially devastate future prospects for students and institutions alike.
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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 | 0.034 | 0.146 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.014 | 0.023 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".