License to Gamble: Discursive Perspectives on the 2019 Reregulation of the Swedish Gambling Market
Bibliographic record
Abstract
During the last decades, several European gambling markets have been reregulated. In 2019, it was Sweden’s turn; the former oligopoly was replaced by a licensing system. In this article, the governmental inquiry in which the new system was proposed, outlined, and justified is studied using discourse analysis. Medical, public health, and free market discourses have been shown to dominate articulations of gambling in several national contexts, but the ways in which these discourses interact, overlap, and differ are crucial to understand better in order to appreciate the production and legitimation of meanings around gambling. Moreover, the 2019 reregulation has not yet been studied from discursive perspectives; thus, the article makes both theoretical and empirical contributions. The article demonstrates that market and medical discourses structure the inquiry. While they sometimes overlap and merge, their co-existence also causes tensions, for instance regarding whether an increase in gambling is acceptable or not. The article points to a strengthening of market and medical discourses and a weakening of public health discussion within Swedish gambling debates.
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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 | 0.020 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.018 | 0.095 |
| Scholarly communication | 0.023 | 0.011 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.009 | 0.010 |
| 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".