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Record W3212429225 · doi:10.1111/anti.12771

Civic‐Led Banishment in South Africa: Punishment, Authority, and Spatialised Precarity

2021· article· en· W3212429225 on OpenAlexafffund
S.J. Cooper‐Knock, Gail Super

Bibliographic record

VenueAntipode · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicCriminal Justice and Corrections Analysis
Canadian institutionsInternational Centre for Comparative CriminologyUniversity of Toronto
FundersUniversity of TorontoEconomic and Social Research CouncilUniversity of EdinburghSocial Sciences and Humanities Research Council of CanadaGovernment of Canada
KeywordsPrecarityPunitive damagesPunishment (psychology)Public spaceLootingSociologyCriminologyState (computer science)LawPolitical scienceSocial psychologyPsychology

Abstract

fetched live from OpenAlex

Abstract Civic‐led banishment, a fundamentally spatial punishment, is an understudied phenomenon in South Africa and beyond. We define it as “a punitive spatial practice, enacted by non‐state actors in response to alleged criminality or deviance, which attempts varying degrees of socio‐spatial expulsion over time”. This definition lays the framework for a socio‐spatial analysis of punishment, and yields insights into the exercise of socio‐spatial control in public and private space. We emphasise the specific challenges associated with banishment, together with the relationship between space, punishment, public authority, and sovereignty. We demonstrate how “negotiations” around banishment trade off two forms of intersecting precarity: those faced by residents in informal settlements and the potential precarity of public authorities. Finally, we argue that an exploration of all forms of punishment through the lens of socio‐spatial expulsion enables us to tap into conversations around penal abolitionism.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.332
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.032
GPT teacher head0.307
Teacher spread0.275 · 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.

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

Citations3
Published2021
Admission routes2
Has abstractyes

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