Safer gambling and consumer protection failings among 40 frequently visited cryptocurrency-based online gambling operators.
Bibliographic record
Abstract
OBJECTIVE: Online gambling has increased the accessibility and range of gambling products available to people all over the world. This trend has been particularly noticeable in the United Kingdom. Cryptocurrency-based gambling is a new, largely unregulated, way to gamble online, which uses mostly anonymous blockchain-based technologies, such as Bitcoin. The present research investigated consumer protection features of 40 frequently visited and U.K.-accessible cryptocurrency-based online gambling operators. METHOD: A content analysis was performed by visiting all 40 cryptocurrency-based online operators and recording their safer gambling and consumer protection practices. Coded features included aspects of the sign-up process, features of any safer gambling pages, customer support practices, and Identity verification. RESULTS: Results revealed significant failings in the account registration process; none of the operators verified the identity of new users, and 35% required only an email or no personal information for sign-up. Overall, 37.5% of operators offered no safer gambling tools and a further 20% offered only one. Additionally, 64.7% of operators continued to email promotional material after being informed of a user's impaired control when gambling. Less than half of the analyzed operators held a valid license (47.5%), and none of the operators with an available deposit page required identity verification before enabling deposits. CONCLUSIONS: These results highlight the potential risks for young and vulnerable individuals, especially when a lack of identity verification is paired with the inherent anonymity of cryptocurrencies. Furthermore, it emphasizes the need for greater policy and research attention toward cryptocurrency-based online gambling. (PsycInfo Database Record (c) 2023 APA, all rights reserved).
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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 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".