Academic Cronyism and Publication in Journal Special Issues: an Exploratory Study
Bibliographic record
Abstract
Abstract While academic cronyism is an acknowledged phenomenon it is rarely the subject of research in higher education except by reference to staff recruitment and academic in-breeding. It constitutes a ‘wicked’ problem that is complex to understand and investigate since it is based on networks of individuals that are grounded on reciprocal professional benefits that are often private or at least partly hidden from view. Drawing on social network theory, this paper demonstrates how academic cronyism works by reference to journal special issues developing illustrative case studies of relationships between authors and special issue editors. Publicly available data from journal home pages, journal special issues, individual bibliometrics, citation systems and social media are used to trace prior and current authorship relationships between contributors to journal special issues. The case studies indicate that academic social networks are a significant factor in respect to journal special issues providing a strong prima facie indicator of academic cronyism. While some journals use the standard peer review process for special issues based on open calls for papers others use irregular procedures and operate a closed system that promotes academic cronyism. It is recommended that a set of principles labeled ‘CORE’ –consistency, openness, rigour, and equity – should be adopted by journals to protect the integrity of journal special issues.
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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 | 0.162 | 0.155 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.060 | 0.065 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.014 |
| 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; 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".