Implicit measures of attitudes toward gambling: An exploratory study
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
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.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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