The psychology of music in gambling environments: An observational research note
Bibliographic record
Abstract
Effects of the listening context on responses to music largely have been neglected despite the prevalence of music in our everyday lives. Furthermore, there has been no research on the role of music in gambling environments (e.g., casinos, amusement arcades) despite gambling's increased popularity as a leisure pursuit. An exploratory observational study in gambling arcades was carried out to investigate (i) how music is used as background music in amusement arcades, and (ii) how slot machines utilize music in their design. Results indicated that arcades often have music that caters for their customer demographics and that this may influence gambling behaviour. Furthermore, music from the slot machine itself appears to produce important impression formations about the machine (i.e., quality of the machine, familiarity, distinctiveness, and the sound of winning). It is suggested that music (whether it is in the gambling environment or in the activity itself) has the potential to be important in the acquisition, development, and maintenance of gambling behaviour. Some preliminary ideas and hypotheses to be tested are offered.
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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 | 0.002 | 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 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".