Bibliographic record
Abstract
This study aimed to conduct a bibliometric analysis of studies on online gambling. Web of Science Core database was used to obtain all publications on online gambling between 1998 and 2024. The bibliometric analysis was confined to studies published up to 2024, given that 2025 is still in progress and the complete body of literature for that year is not yet accessible. The bibliometric analysis was conducted using the VOSviewer program. The study initially determined that the number of studies in the domain of online gambling exhibited an overall increase from 1998 to 2024, with a few exceptions. A co-authorship analysis indicates that England is the most prolific nation in this field of study. Subsequently, Australia, Canada, the United States of America, and Spain are in descending order of productivity. The analysis yielded the conclusion that Nerilee Hing is the most influential researcher in the field of online gambling. The results of the cooccurrence analysis indicated that the most frequently used keywords were related to "gambling", "online gambling", "problem gambling", "responsible gambling", "addiction", "COVID-19", and "adolescent". In this study, it was concluded that research on online gambling addiction has increased over the years and that online gambling addiction has been emphasized in different cultures.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
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 | 0.013 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.276 | 0.297 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.003 |
| 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, unvalidatedLabeled directly by 2 models reading the full record.
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".