The Pains of Imprisonment in a Pandemic
Bibliographic record
Abstract
This article examines how the law of punishment has responded to the impact of the COVID-19 pandemic on jails and prisons. While detention has become more severe and risky for all who live and work in correctional institutions, there has been significant variation in judicial willingness to recognize these systemic impacts. Often courts limit protection to those able to adduce evidence that they will become seriously ill or die from COVID-19. First, the authors discuss the approach taken by individual judges to bail, observing (1) cases where judges take judicial notice of the heightened risks and severity of imprisonment for all inmates during the pandemic, and (2) cases that require the accused to establish that they are at increased risk before COVID-19 can weigh heavily on the decision to detain. Second, the authors discuss a similar story of variation in how judges have responded to the effect that pandemic conditions should have on the calculation of credit for pretrial detention. Finally, they discuss the impact that COVID-19 has had on sentencing, where judges are more willing to consider how the pains of imprisonment have been intensified during the pandemic in a way that impacts the question of a fit or proportionate sentence of custody. The authors conclude that the use of individual vulnerability as a prerequisite is a flawed halfway measure given the impacts of COVID-19 on our institutions of detention and punishment. They conclude further that a proper understanding of those impacts may help to facilitate better understanding of the risks and effects of detention that predate the pandemic and will outlast it.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.006 | 0.012 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".