Using crowdsourcing to examine behavioral economic measures of alcohol value and proportionate alcohol reinforcement.
Bibliographic record
Abstract
Online crowdsourcing websites such as Amazon's Mechanical Turk (MTurk) are increasingly being used in addictions research. However, there is a relative paucity of such research examining the validity of administering behavioral economic alcohol-related measures, via an online crowdsourcing platform. This study sought to validate an alcohol purchase task (APT) for assessing demand and a questionnaire measure of proportionate alcohol reinforcement, using an online sample of participants recruited via MTurk. Participants (N = 865, 59% female) were recruited via MTurk to complete the APT, proportionate alcohol reinforcement questionnaire, Alcohol Use Disorders Identification Test (AUDIT), and demographics. Responses on the APT were highly systematic (<3% nonsystematic data) and conformed to prototypical demand curves. Correlation analyses revealed significant associations among AUDIT total scores with a majority of the alcohol demand indices (r values .08-53, p values < .05) as well as proportionate alcohol reinforcement, r = .43, p < .001. Regression analyses controlling for relevant covariates indicated that intensity, BP, Omax, elasticity, and reinforcement ratio predicted significant variance in AUDIT scores. This study further supports the use of online crowdsourcing websites for investigating behavioral economic determinants of alcohol misuse. (PsycINFO Database Record
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".