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Record W3147253629 · doi:10.1080/13621025.2021.1903396

Reclaiming citizenship from police violence

2021· article· en· W3147253629 on OpenAlexafffund
Michelle D. Bonner

Bibliographic record

VenueCitizenship Studies · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicPolicing Practices and Perceptions
Canadian institutionsUniversity of Victoria
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsCitizenshipArgument (complex analysis)SociologyIdentity (music)Meaning (existential)Power (physics)CriminologySituatedEconomic JusticeInterdependenceGender studiesPolitical scienceLawEpistemologySocial scienceAestheticsPolitics

Abstract

fetched live from OpenAlex

Police violence contributes to uneven experiences of citizenship. Identity often determines who is affected and the ability of loved ones to fight back. While reforms to the criminal justice system are important, I argue that police violence is best understood as situated within public contestations over the meaning and practice of citizenship. These are multilevel and interdependent contestations that occur at the interpersonal, discursive, and judicial levels and are embedded in power structures that aim to reproduce existing social inequalities. Through contestation, targeted communities, at the very least, create a dissensus on the practices that exclude them and, at best, contribute to the reconstruction of the meaning of citizenship in ways that include their voice. This argument is grounded in the literature on police violence in Latin America and in an in-depth qualitative analysis of the case study of the enforced disappearance of Luciano Arruga in Argentina in 2009.

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)0.004
metaresearch head score (Gemma)0.015
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.010
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.015
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0100.025
Scholarly communication0.0070.005
Open science0.0010.011
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0020.000

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 head0.179
GPT teacher head0.421
Teacher spread0.241 · 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 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

Citations8
Published2021
Admission routes2
Has abstractyes

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