COVID-19 and European carcerality: Do national prison policies converge when faced with a pandemic?
Bibliographic record
Abstract
The article analyses an original dataset on policies adopted in 47 European countries between December 2019 and June 2020 to prevent coronavirus from spreading to prisons, applying event-history analysis. We answer two questions: 1) Do European countries adopt similar policies when tackling the COVID-19 pandemic in prisons? 2) What factors are associated with prison policy convergence or divergence? We analyze two policies we identified as common responses across prisons around the world: limitations on visitation rights for prisoners, and early releases of prisoners. We found that all states in our sample implemented bans on visits, showing policy convergence. Fewer countries (16) opted for early releases. Compared to the banning of visitation, early releases took longer to enact. We found that countries with prison overcrowding problems were quicker to release or pardon prisoners. When prisons were not overcrowded, countries with higher proportions of local nationals in their prisons were much faster to limit visits relative to prisons in which the foreign population was high. This research broadens our comparative understanding of European carcerality by moving the comparative line further East, taking into account multi-level governance of penality, and analyzing variables that emphasize the 'society' element of the 'punishment and society' nexus.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".