The problem of demos – limitation of an active suffrage for a convicted person who committed a particularly serious crime
Bibliographic record
Abstract
The problem of disenfranchising individuals convicted of particularly serious crimes presents significant challenges to democratic principles. While democracy emphasizes equal participation, certain exceptions – such as depriving individuals of voting rights – may be justified under specific conditions, including criminal convictions. This paper analyzes the legitimacy and impact of such limitations, focusing on the proportionality of disenfranchisement and its effects on democratic governance. It compares international practices, including those in Canada, South Africa, and various European countries, to highlight divergent approaches to prisoners’ voting rights. Special attention is given to key rulings by the European Court of Human Rights, including the cases of Hirst, Frodl, and Scoppola, which stress the importance of proportionality and individual circumstances in restricting suffrage. The analysis concludes that blanket bans on voting undermine rehabilitation efforts and democratic values, advocating for personalized assessments to balance justice, democracy, and human rights.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.013 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".