Bibliographic record
Abstract
Abstract Criminal law theorists have for the most part neglected the question of why imprisonment requires special legal safeguards for those targeted. The few scholars who have addressed this question have focused on how prison facilities restrict freedom of movement, control over one’s daily life, and access to particular human functioning, but they have ignored state measures that do not include confining individuals behind bars. I defend an alternative account: I argue that the use of prison facilities is part of a raft of state measures, which includes measures that have a relatively minor impact on individuals and others that are more severe. What matters, in deciding what legal safeguards individuals should have against what kinds of state imposition, is how severely a measure impacts on the normal life of those subjected to it. This impact-based approach enables us to decouple the concept of imprisonment from walls, locks, and political and social barriers, thereby highlighting atypical forms of imprisonment, such as open prisons, as well as potential forms of imprisonment to be employed outside of prison, including house arrest. For instance, a person confined to a prison facility can, to a certain extent, be free to take part in society, or a person subject to other state measures can reside at home but be constrained from carrying out a major part of her activities. The account I defend enables us to identify imprisoned individuals as well as those subjected to measures similar to imprisonment, which has potential consequences for their legal 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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 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".