Patterns of Disciplinary Involvement and Academic Collaboration in Gambling Research: A Co-Citation Analysis
Bibliographic record
Abstract
The purpose of this study was to investigate the current academic research foci in peer-reviewed studies on gambling. The researchers used co-citation analysis as a bibliometrics method. All the gambling-related publications indexed in Scopus and Web of Science were identified, and their citation patterns were analyzed. Our dataset includes a total of 2418 peer-reviewed gambling studies published over the five-year period from 2014–2018. The VOSviewer tool was used to visualize bibliometric networks and reveal key clusters among the studies. The findings indicate that gambling researchers mostly cited authors from the disciplines of neuroscience, psychology, health science, and psychiatry. Only 2% of the cited authors were from other disciplines, such as those in the social sciences and humanities. The most frequently cited sources also reveal the same pattern: that gambling researchers mostly cited articles published in neuroscience, psychology, and health science journals. The publications reviewed deal mainly with the pathological and treatment aspects of gambling. We also discovered some unique patterns of citation and collaboration, focusing on topics such as videogames, social network games, family, business, and tourism.
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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 | 0.019 | 0.130 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.122 | 0.209 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".