An Empirical Attempt to Operationalize Chasing Losses in Gambling Utilizing Account-Based Player Tracking Data
Bibliographic record
Abstract
In recent years, account-based player tracking data have been utilized as a potential tool to identify problem gambling online and associated markers of harm. One established marker of harm among problem gamblers is chasing losses, and chasing losses is a key criterion for gambling disorder in the most recent edition of the Diagnostic and Statistical Manual of Mental Disorders. Given the paucity of research with respect to chasing losses among online casino players using account-based data, the present study developed five metrics that may be indicative of chasing behavior: These were (i) within-session chasing, (ii) across-session chasing, (iii) across-days chasing, (iv) regular gambling account depletion, and (v) frequent session depositing. The authors were given access by a European online casino to raw data of all players who had placed at least one bet or wagered at least once during December 2021 (N = 16,771 players from the UK, Spain, and Sweden). Results indicated that frequent session depositing reflected chasing losses better than any of the other four metric operationalizations used. While frequent session depositing appears to be more indicative of chasing losses than the other four metrics, all the metrics provide useful information which can be used to help identify problematic gambling behavior online.
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How this classification was reachedexpand
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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".