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Record W4306290714 · doi:10.1007/s11572-022-09647-3

Imprisonment

2022· article· en· W4306290714 on OpenAlexfundno aff
Hadassa Noorda

Bibliographic record

VenueCriminal Law and Philosophy · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicCriminal Justice and Corrections Analysis
Canadian institutionsnot available
FundersNederlandse Organisatie voor Wetenschappelijk OnderzoekYork University
KeywordsImprisonmentPrisonPolitical scienceLife imprisonmentPoliticsState (computer science)LawCriminologySociologyLaw and economicsPsychology

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.915
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.035
GPT teacher head0.298
Teacher spread0.263 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations4
Published2022
Admission routes1
Has abstractyes

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