Is the economic model of gambling dependent on problem gambling? Evidence from an online survey of regular sports bettors in Britain
Bibliographic record
Abstract
Understanding how the gambling industry generates revenue is of paramount importance. Questions about whether higher volumes of expenditure are concentrated among a small proportion of gamblers, and how this varies by problematic gambling status, underpin policy debate about consumer protection. Analyzing data from two timepoints (T0; T2) from a British longitudinal study of regular sports bettors, we explored both for total (gross) spend and gross spend on individual activities: (a) the concentration of self-reported spend on gambling among individuals; and (b) the extent to which spending was disproportionately generated by those with elevated Problem Gambling Severity Index (PGSI) scores. Results showed that gross gambling expenditure was unequal (GINI-coefficient >0.70 for most activities). At both timepoints, those with a PGSI score of 3+ had an elevated share of spending: at T2, 14.1% of PGSI 3+ gamblers accounted for 43.5% of gross gambling spend. There were differences by activity: lotteries displayed less reliance on those with a PGSI score of 3+ whereas this group contributed over 80% of gross spend on online casinos. Policy attention should focus on reframing the underlying economic model on which some gambling activities are predicated, creating more equal patterns of consumption and less reliance on those harmed.
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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 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) | 0.004 | 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".