Midbrain Mutiny: The Picoeconomics and Neuroeconomics of Disordered Gambling
Bibliographic record
Abstract
Disordered gambling has been on the radar of researchers and clinicians for decades. It gained acceptance as a diagnostic entity approximately 30 years ago with the publication of DSM–III (American Psychiatric Association, 1980). However, from this reviewer’s perspective, the condition of disordered gambling has long been underappreciated with respect to the prevalence of the disorder or the level of devastation with which it is associated. Several factors most likely underlie the increased interest in the condition. First, with the advent of online gambling and easier access to land-based gambling venues in many countries, more people are affected by the disorder. Second, a growing appreciation for the existence of behavioral addictions has emerged amongst researchers, clinicians, and laypeople. Third, the advent of technologies that enable researchers and clinicians to characterize brain structure (e.g., magnetic resonance imaging, or MRI) and function (e.g., fluorodeoxyglucose positron emission tomography, or FDG–PET) have spurred an interest in the study of addiction. Finally, as the field of neuroeconomics has gained traction in the scientific community as a legitimate area of study, there has been increased interest in the related phenomenon of disordered gambling.
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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 | 0.002 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.010 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 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".