Commentary on: Are we overpathologizing everyday life? A tenable blueprint for behavioral addiction research
Bibliographic record
Abstract
Billieux et al. (2015) propose that the recent proliferation of behavioral addictions has been driven by deficiencies in the underlying research strategy. This commentary considers how pathological gambling (now termed gambling disorder) traversed these challenges to become the first recognized behavioral addiction in the DSM-5. Ironically, many similar issues continue to exist in research on gambling disorder, including question-marks over the validity of tolerance, heterogeneity in gambling motives, and the under-specification of neuroimaging biomarkers. Nevertheless, I contend that the case for gambling disorder as a behavioral addiction has been bolstered by the existence of clear and consistent functional impairment (primarily in the form of debt), coupled with the development of a public health approach that has given emphasis to product features (i.e. the structural characteristics of gambling forms) as much as individual dispositions (the 'addictive personality').
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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 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.055 | 0.059 |
| Insufficient payload (model declined to judge) | 0.008 | 0.008 |
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".