A New Chapter in the European Court of Justice Gambling Saga: A Stacked Deck?
Bibliographic record
Abstract
And I believed in my system … within a quarter of an hour I won 600 francs. This whetted my appetite. Suddenly I started to lose, couldn't control myself and lost everything. After that I … took my last money, and went to play … I was carried away by this unusual good fortune and I risked all 35 napoleons and lost them all. I had 6 napoleons left to pay the landlady and for the journey. In Geneva I pawned my watch. Whether or not consumers should be protected against their propensity for gambling addictions is a public policy decision that is left exclusively to the individual Member State. Case law of the Court of Justice of the European Union (CJEU) has developed 'high trust' principles that provide Member States wide discretion to determine what system of protection - if any - works best in their territory. However, if a Member States policy turns out to be inconsistent with its policy aims and restricts free movement, the Court is not scared to strike it down. The latest judgments in the gambling saga build upon sixteen years of case law. This article investigates whether Member States are by now dealt with a stacked deck of principles.
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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 | 0.013 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.014 | 0.014 |
| Scholarly communication | 0.024 | 0.011 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.034 | 0.024 |
| Insufficient payload (model declined to judge) | 0.006 | 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".