Influence of Neutralization Attitude in Academic Dishonesty among Undergraduates
Bibliographic record
Abstract
Previous literature had proposed that individuals tend to use neutralization to motivate their decisions to engage in deviant behaviours. This indicated that even though students have strong motivations not to cheat may do so anyway after employing neutralizing strategies. Hence, this study attempted to examine the role of neutralization in influencing students’ attitude towards academic dishonesty. Students tend to use neutralization technique in order to free themselves from feeling guilty in engaging academic dishonesty. Besides that, it also attempted to study the reasons behind college student academic cheating behaviours. This study employed 620 randomly selected students from six different academic institutions. Results supported that students who engaged in academic dishonesty differ significantly from those who did not engage in this deviant behaviour with respect to their tendency to neutralize cheating. Results showed that cheating and neutralization were positively correlated among students. Through the findings, it showed that the use of neutralization techniques explained why students acknowledged that cheating is wrong but still chose to do it anyway.
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How this classification was reachedexpand
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 | 0.001 | 0.003 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".