Area under the curve as a novel metric of behavioral economic demand for alcohol.
Bibliographic record
Abstract
Behavioral economic purchase tasks can be readily used to assess demand for a number of addictive substances, including alcohol, tobacco, and illicit drugs. However, several methodological limitations associated with the techniques used to quantify demand may reduce the utility of demand measures. In the present study, we sought to introduce area under the curve (AUC), commonly used to quantify degree of delay discounting, as a novel index of demand. A sample of 207 heavy-drinking college students completed a standard alcohol purchase task and provided information about typical weekly drinking patterns and alcohol-related problems. Level of alcohol demand was quantified using AUC--which reflects the entire amount of consumption across all drink prices--as well as the standard demand indices (e.g., intensity, breakpoint, Omax, Pmax, and elasticity). Results indicated that AUC was significantly correlated with each of the other demand indices (rs = .42-.92), with particularly strong associations with Omax (r = .92). In regression models, AUC and intensity were significant predictors of weekly drinking quantity, and AUC uniquely predicted alcohol-related problems, even after controlling for drinking level. In a parallel set of analyses, Omax also predicted drinking quantity and alcohol problems, although Omax was not a unique predictor of the latter. These results offer initial support for using AUC as an index of alcohol demand. Additional research is necessary to further validate this approach and to examine its utility in quantifying demand for other addictive substances such as tobacco and illicit drugs.
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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 | 0.005 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".