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 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".