Implicit measures of attitudes toward gambling: An exploratory study
Bibliographic record
Abstract
Gambling researchers have used self-report measures in order to assess gamblers' attitudes toward gambling. Despite their efficiency, self-report measures of attitudes often suffer self-presentation and social desirability bias when they are used to assess socially sensitive or stigmatized issues. This concern has led to the recent development of indirect, non-reactive measures of attitudes in psychology. These implicit measures of attitudes tend to reveal automatic, impulsive mental processes, whereas the self-report measures tap conscious, reflective processes (F. Strack & R. Deutsch, 2004). In this paper, we demonstrate how response latency-based measures can be used to investigate attitudes toward gambling. We report findings of our empirical study, in which evaluative priming (Fazio et al., 1995) and the Single Category Implicit Association Test (SC-IAT; Karpinski & Steinman, 1996) were used to assess implicit attitudes toward gambling, and the Single Target IAT was adapted to assess implicit arousal-sedation associations of gambling. With a sample of 102 undergraduate students, we found that latency-based measures of attitudes toward gambling were not significantly correlated with self-report measures. Moderate-to-high-risk gamblers held more positive attitudes toward gambling in the SC-IAT and exhibited more positive and more negative attitudes toward gambling in the evaluative priming task than did low-risk gamblers.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".