From plagiarism to predatory publishing: Organizational factors in explaining academic misconduct
Bibliographic record
Abstract
Our study contributes to the literature pertaining to plagiarism that focuses on the systemic factors underlying academic misconduct. While most such research has been conducted in Western countries, such as the US, UK, Australia, and Canada, we collected data from the academic periphery. On the country level, the incidence of plagiarism is higher in the academic periphery . However, these country level differences can conceal the variance within national academic communities; consequently, we focus on organizational characteristics as a potential source of influence in this context. We find that organizational factors allow us to predict reported academic misconduct better in the context of predatory publishing, while such factors are less relevant for predicting the incidence of academic plagiarism. An exception to this general rule is the possibility of sanctions that an organization would implement in the case of misconduct. A lack of organizational sanctions is positively associated with reported questionable research practices, with respect to both publishing in predatory journals and academic plagiarism.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.005 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.056 | 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; both teacher heads agree on what is shown here.
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".