Editorial: Problem Gambling: Summarizing Research Findings and Defining New Horizons
Bibliographic record
Abstract
More than a decade ago, Shaffer et al. (2006) reported that gambling-related research was growing at an exponential rate. Since that time, this trend appears to have continued, and much more is now known about this particular form of risky behavior. Nevertheless, there is still a general tendency to not perceive gambling as a potential danger for youth and other vulnerable populations. The latest edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-5) included “gambling disorder” as the only condition in the section “non-substance-related disorders.” Moreover, it was specified that this disorder can indeed occur in adolescence, young adulthood or even late adulthood. Despite this fact, theoretical and applied research on problem gambling especially with regard to adolescence and other risk groups still remains fragmentary. For this reason, we felt it to be important to organize a special research topic on gambling. The primary goals were to highlight the necessity of considering excessive gambling as a potential harmful activity, to summarize the state-of-art of international research on different aspects of the topic and to offer important novel findings relevant for advancing knowledge in the field of gambling. Taken together, the contributions can be classified into four broad categories: (1) youth gambling, (2) risk factors in adulthood, (3) measurement issues, and (4) clinical research.
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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 | 0.006 | 0.030 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.015 | 0.017 |
| Insufficient payload (model declined to judge) | 0.011 | 0.010 |
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".