Within-session chasing of losses and wins in an online eCasino
Bibliographic record
Abstract
Chasing refers to the escalation of betting behaviour. It is conventionally seen when losing (loss chasing) but can also be seen after wins. Diagnostic and screening items for gambling problems describe chasing as returning ‘another day’ to gamble, i.e. between-session chasing. However, gamblers may also chase outcomes within sessions, and this may be particularly relevant in the online gambling environment. The current study focused on two expressions of within-session chasing: i) increasing the bet amount, or ii) a reduced probability of quitting the session, as a function of prior losses or wins. These expressions were examined across five categories of online gambling products: slot machines, probability games, blackjack, video poker, and roulette. For losses, gamblers tended to bet more, and played longer sessions, after immediate losses, but they reduced their bet and played shorter sessions when losing cumulatively. The reversed patterns in the cumulative model may be due to financial constraints on the gambler. For wins, gamblers played shorter sessions as a function of both immediate and cumulative wins, but they also increased the bet amount when winning. Chasing patterns were fairly similar across the different product categories, and we saw limited evidence for our hypothesis that chasing is greatest for online slot machines as an established high-risk category. Overall, chasing was seen to be a multi-faceted construct, varying across these two behavioural expressions, by the immediate or cumulative timeframe of prior outcomes, and by game type.
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.
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 | 0.006 |
| 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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".