Resilience and autonomy at stake: The public construct of the Paf gambling company in the Åland Islands community
Bibliographic record
Abstract
The gambling business entails geo-economic opportunities for islands, especially in times of online gambling. However, it also involves risks like ill mental health, debt, and social problems. Furthermore, a heavy reliance on gambling revenues involves great moral dilemmas, especially when the gambling provision is operated within a not-for-profit public regime. This study concerns how these aspects are negotiated in the public discussion in Åland Islands, an autonomous group of islands situated between Finland and Sweden. By ruling of its regional parliament and the Finnish Lotteries Act, the Åland-based gambling monopoly company Ålands penningautomatförening (Paf) has the right to provide onshore gambling on the Islands, on the Internet, and on cruise ships trafficking the Baltic Sea. The study examines Paf’s role as a pillar of the local community, and the ways in which this position is sustained and contested. By analyzing a corpus of 862 online texts from local newspapers and public radio services from 2006–2018, this study demonstrates how Åland depends on an incongruous public construction of Paf as a responsible actor that is simultaneously criticized for not exercising greater transparency and responsibility, highlighting a contradiction between the provision of harmful gambling products and economic benefits for the community.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.016 | 0.035 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| 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".