Exploring the Link between Academic Dishonesty and Economic Delinquency: A Partial Least Squares Path Modeling Approach
Bibliographic record
Abstract
This paper advances the study of the relationship between the attitude towards academic dishonesty and other types of dishonest and even fraudulent behavior, such as tax evasion and piracy. It proposes a model in which the attitudes towards two types of cheating and fraud are systematically analyzed in connection with a complex set of latent construct determinants and control variables. It attempts to predict the tolerance towards tax evasion and social insurance fraud and piracy, using academic cheating as the main predictor. The proposed model surveys 504 student respondents, uses a partial least squares—path modeling analysis, and employs two subsets of latent constructs to account for context and disposition. The relationship between the outcome variable and the subset of predictors that account for context is mediated by yet another latent construct—Preoccupation about Money—that has been shown to strongly influence people’s attitude towards a whole range of social and economic behaviors. The results show academic dishonesty is a statistically significant predictor of an entire range of unethical and fraudulent behavior acceptance, and confirm the role played by both contextual and dispositional variables; moreover, they show that dispositional and contextual variables tend to be segregated according to how they impact the outcome. They also show that money priming does not act as a mediator, in spite of its stand-alone impact on the outcome variables. The most important result, however, is that the effect size of the main predictor is large. The contribution of this paper is two-fold: it advances a line of research previously sidestepped, and it proposes a comprehensive and robust model with a view to establish a hierarchy of significance and effect size in predicting deviance and fraud. Most of all, this research highlights the central role played by academic dishonesty in predicting the acceptance of any type of dishonest behavior, be it in the workplace, at home, or when discharging one’s responsibilities as a citizen. The results presented here give important clues as to where to start intervening in order to discourage the acceptance of deviance and fraud. Educators, university professors, and academic administrators should be at the forefront of targeted campaigns and policies aimed at fighting and reducing academic dishonesty.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".