Identifying electronic gaming machine gambling personae through unsupervised session classification
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
The rising accessibility in gambling products, such as Electronic Gaming Machines (EGM), has increased interest in the effects of gambling; in particular, the potential for impulse control disorders, such as problem gambling. Nevertheless, empirical research of EGM gambling behaviour is scarce. In this exploratory study, we apply data mining techniques on 46,416 gambling sessions, collected in situ from 288 EGMs. Our research focused on identifying the at-risk behavioural markers of sessions to help distinguish gambling personae. Our data included measures of gambling involvement, out-of pocket expense of sessions, amount won, and cost of gambling. This research, discusses the methodology used to collect and analyze the required gambling measures, explains the criteria used for identifying valid sessions, and combines outlier mining methods to identify instances of heavily involved gambling (i.e., outliers). Our results suggest that sessions were classified as potential non-problem, potential low-risk, potential moderate risk, and potential problem gambling sessions. Further, outlier sessions were more heavily involved in terms of gambling intensity and amount redeemed, despite having low duration times. Finally, our methods suggest that the lack of player identification does not prevent one from identifying the potential incidence of problem gambling behaviour.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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