Online Gambling, Regulation, and Risks: A Comparison of Gambling Policies in Finland and the Netherlands
Bibliographic record
Abstract
The purpose of this article is to compare the different regulatory approaches taken by Finland and the Netherlands in response to the pressures of European Union law, unlicensed gambling, and the harmful effects which can arise from gambling. The two Member States represent two different models of gambling regulation. According to Kingma, the models refer to different attitudes and concerns towards gambling in different timeframes. We argue that Finland fits the “alibi model” of gambling regulation, whereas the Netherlands aligns with the “risk model”. Both countries have decided to restrict the cross-border movement of gambling services, even though Finland has opted for a monopoly system and the Netherlands is heading towards a licensing system. We employ the “Multiple Streams Approach” to explain why Finland and the Netherlands have taken different political and legislative paths in the regulation of gambling services. For several years, Finnish gambling policy has focused on channeling demand towards domestic online gambling sites, which have been represented as more secure than foreign online gambling sites. The Netherlands seeks to channel 80 percent of demand to locally licensed online operators. Both Finland and the Netherlands seek the same objective: to protect consumers from the excesses of gambling in part by reducing the presence of unlicensed operators in their respective national markets.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".