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Record W4394010989 · doi:10.1177/09670106241230431

Police work and the politics of expendability in India

2024· article· en· W4394010989 on OpenAlexaff
Beatrice Jauregui

Bibliographic record

VenueSecurity Dialogue · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicCrime, Illicit Activities, and Governance
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsPoliticsWork (physics)Political scienceSociologyPublic administrationCriminologyLawEngineering

Abstract

fetched live from OpenAlex

Abstract This article examines how rank-and-file police in contemporary India express work-related grievances regarding official neglect of their well-being, systemic exploitation by government authorities and other elites, and routinized threats of bodily harm and death. It analyzes these experiences as manifestations of a ‘politics of expendability’ through which police, conceived as security laborers, are ironically condemned to exclusion from a morally and materially ‘good life’, and only partially or superficially compensated for the often questionably licit kinds of work demanded of them. Conceiving this politics as both intersecting with and reflective of broader structures of systemic inequality and oppressive violence, I consider recent cases of constables publicly complaining about their working and living conditions through social media, quitting their jobs, and dying by suicide as signs of resignation-cum-protest. In so doing, I demonstrate how the social demands for police work as security labor are co-configured with a devaluation of police life that produces affects of despair and structures of disposability. Rethinking the globalized paradox of police demonization-cum-valorization, this study raises challenging questions about how police in India – as well as in other contexts, and especially in Global South postcolonies – may be conceived as expendable workers. It further considers how, moving forward, we must work to reimagine what policing as institutionalized security labor and police work are – and ought to be.

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)0.001
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: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.293
Threshold uncertainty score0.964

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.015
GPT teacher head0.289
Teacher spread0.274 · 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 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

Citations5
Published2024
Admission routes1
Has abstractyes

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