Midbrain Mutiny: The Picoeconomics and Neuroeconomics of Disordered Gambling
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it