Use of online crowdsourcing platforms for gambling research
Bibliographic record
Abstract
Crowdsourcing platforms like Amazon’s Mechnical Turk and Crowdflower have been touted to be a cost-effective way to collect large amounts of behavioural data. Across four large-n studies, gambling-related behaviours, tendencies and traits among participants in these labour markets were examined. In Studies 1 and 2, both conducted on Crowdflower, problem gamblers (as measured by the benchmark Problem Gambling Severity Index) comprised 24.5% and 21.9% of participants, respectively. In Study 3, conducted on Mechanical Turk, problem gamblers comprised 9.0% of participants. In Study 4, a two-wave longitudinal study conducted on Crowdflower, problem gamblers comprised 13.5% of participants in wave one and 14.8% of participants in wave two. In Studies 2 and 3, strong convergent associations were demonstrated across various measures of problem gambling tendencies and general gambling involvement. Furthermore, it was demonstrated that gambling was associated with personality traits (impulsivity, sensation-seeking, self-control), risk attitudes, affect, and behavioural risk-taking consistent with previous research. In Study 4, it was demonstrated that measures of problem gambling have acceptable test-retest reliability. Online crowdsourcing platforms appear to offer access to samples with remarkably high proportions of problem gamblers. However, this characteristic means that such samples are not necessarily representative of gambling tendencies among more general populations.
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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 | 0.017 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".