Gambling, youth and the internet: should we be concerned?
Bibliographic record
Abstract
INTRODUCTION: The recent growth of gambling problems among youth around the world is alarming. Researchers, clinicians, educators and the public have only begun to recognize the significance of this risky adolescent behaviour. With the continuous rise in gambling technology and the expansion of the gambling industry, more gambling opportunities exist today than ever before. METHOD: The literature on gambling and youth was reviewed. RESULTS: Given the greater accessibility, availability, and promotion of gambling, more and more youth have become attracted to the perceived excitement, entertainment, and financial freedom associated with gambling. While Internet gambling is a recent phenomenon that remains to be explored, the potential for future problems among youth is high, especially among a generation of young people who have grown up with videogames, computers, and the Internet. CONCLUSION: Our current knowledge and understanding of the seriousness of gambling problems, its magnitude, and its impact on the health and well-being of children and youth compels us to respond to these new forms of gambling in a timely and effective manner.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.007 | 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".