Bibliographic record
Abstract
This chapter examines European disenfranchisement policies, aiming to uncover their characteristics and use them as models for the normative discussion. It considers the legislation in 43 European countries along four dimensions: the prevalence of restrictions; dominant notions of “disenfranchise-able” offender; extent of restrictions; and timing, length, and manner of imposition of restrictions. The analysis uncovers a great deal of diversity across Europe: while the rights of many criminal offenders remain intact, most countries nevertheless believe that some instances of criminal offending warrant restrictions. The chapter finds that three-quarters of European countries impose some restrictions, one-third disenfranchise all prisoners, one-half restrict both active and passive electoral rights, one-third employ post-penal disenfranchisement, while one-quarter permit a permanent ban. Comparing these data to the US states—which are often considered incomparably strict—the chapter suggests that the difference is only in the degree of restrictions and not in the kind of existing policies.
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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 | 0.002 | 0.006 |
| Science and technology studies | 0.005 | 0.022 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".