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Record W4377023886 · doi:10.1017/s0003055423000400

The Right to Hunger Strike

2023· article· en· W4377023886 on OpenAlexfundno aff
Candice Delmas

Bibliographic record

VenueAmerican Political Science Review · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicTorture, Ethics, and Law
Canadian institutionsnot available
FundersFreie Universität BerlinUniversità degli Studi di GenovaUniversity of TorontoUniversiteit UtrechtAgence Nationale de la RechercheUniversity College LondonNorthwestern UniversityKing's College LondonBrown University
KeywordsRedressGrievanceOppressionArgument (complex analysis)Political scienceLawPrisonCriminologySociologyPoliticsMedicine

Abstract

fetched live from OpenAlex

Hunger strikes are commonly repressed in prison and seen as disruptive, coercive, and violent. Hunger strikers and their advocates insist that incarcerated persons have a right to hunger strike, which protects them against repression and force-feeding. Physicians and medical ethicists generally ground this right in the right to refuse medical treatment; lawyers and legal scholars derive it from incarcerated persons’ free speech rights. Neither account adequately grounds the right to hunger strike because both misrepresent the hunger strike as noncoercive and nonviolent. I articulate an alternative, dual account of the right to hunger strike. On the remedial argument, the right to hunger strike should be legally protected as a right to petition for redress, in light of incarcerated people’s structural vulnerability to abuse and given inadequate grievance mechanisms. The constructive argument derives the right to hunger strike from the right to resist oppression and stresses the normative permissibility of the use of coercive tactics to defend one’s liberty interests in the face of carceral oppression.

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 imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.007
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.005
Scholarly communication0.0030.002
Open science0.0010.002
Research integrity0.0040.004
Insufficient payload (model declined to judge)0.0050.002

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.

Opus teacher head0.042
GPT teacher head0.424
Teacher spread0.382 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations21
Published2023
Admission routes1
Has abstractyes

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Same venueAmerican Political Science ReviewSame topicTorture, Ethics, and LawFrench-language works237,207