Bibliographic record
Abstract
This article argues that scratchcards are not an extension of the online U.K. National Lottery but an entirely different form of gambling, with its own implications for future gambling policy. It also argues that scratchcards are potentially addictive and should be considered a "hard" form of gambling. The author suggests that scratchcard gambling could become a repetitive habit for some people because of their integrated mix of conditioning effects, rapid event frequency, short payout intervals and psychological rewards coupled with the fact that scratchcards require no skill and are highly accessible, deceptively inexpensive and available in "respectable" outlets. On March 21, 1995, Camelot - the consortium that runs the U.K. National Lottery online - introduced scratchcards. Like the online game, 28% of ticket sales contribute towards "good causes" distributed by the National Lotteries Charities Board. Although scratchcards are not new to the United Kingdom, many people view them as intricately linked with the National Lottery. Camelot's scratchcards were the first to benefit from both heavy advertising (television, national newspapers, billboards, etc.) and large jackpots (e.g., £50,000), which meant they became successful very quickly.
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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 | 0.001 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".