Minimising the impact of gambling in the subtle degradation of democratic systems <xref ref-type="note" rid="fn1"><sup>1</sup></xref>
Bibliographic record
Abstract
Gambling can harm a society's social and economic systems and negatively affect its political ecology. If not protected, democratic processes and institutions in jurisdictions with high levels of gambling are likely to undergo a progressive, cumulative degradation of function. These subtle, diffuse distortions result when a broad variety of individuals, working in isolation and reacting to pressures from gambling providers, incrementally compromise their roles and responsibilities. This article examines how these degradations can occur for people working in universities, government departments, media outlets, politics, and community organisations. It argues that any strategy to minimise harm from gambling should include explicit measures to protect the public from such distortions to democratic processes. The single most effective way to do this is to independently monitor people with public duties who have relationships to the beneficiaries of gambling consumption. The article concludes by proposing an international charter that sets benchmark standards for protecting a society from such degradations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".