Gambling Industry Strategies to Influence the Reform of State Online Monopolies
Bibliographic record
Abstract
The aim of this qualitative study was to investigate the strategies used by the gambling industry to influence the reforming of the state online monopoly into a licensing system in Sweden in 2019, and to weaken state online monopoly in Finland. Methodologically, this study used primary data from 9 expert interviews in both countries and secondary data from prior literature, which were analyzed using thematic content analysis. The results identified five main political strategies used by the gambling industry: (1) Information, through lobbying politicians; (2) Constituency Building, through forming an alliance with interest groups; (3) Policy Substitution, through promoting alternative policies and self-regulation; (4) Legal Infringements; and (5) Regulatory Redundancy. The study concluded that the involvement of the gambling industry in policy-making influenced the change of the state online monopoly into a licensing system in Sweden in 2019 and is weakening the state online monopoly in Finland.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.009 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| 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".