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Record W2090879899 · doi:10.1177/1362480613508424

States, subjects and sovereign power: Lessons from global gun cultures

2013· article· en· W2090879899 on OpenAlexafffund
Jennifer Carlson

Bibliographic record

VenueTheoretical Criminology · 2013
Typearticle
Languageen
FieldSocial Sciences
TopicGun Ownership and Violence Research
Canadian institutionsUniversity of Toronto
FundersUniversity of Toronto
KeywordsPrinciple of legalitySovereigntyGovernmentalitySubject (documents)RationalityState (computer science)MonopolyPower (physics)PoliticsState of exceptionSociologyLawSovereign statePolitical scienceCriminologyPolitical economyEconomicsMarket economy

Abstract

fetched live from OpenAlex

This article examines demand for guns for personal protection in the USA, South Africa, and India. To make sense of pro-gun sentiment across these different contexts, I argue that gun owners and carriers who arm themselves for personal protection represent a particular kind of ‘responsibilized’ subject. Drawing on Foucault’s analysis of sovereign power and governmentality, I develop a theory of the ‘sovereign subject’. This is a political rationality marked by private individuals’ capacity and desire to perform sovereign functions that the state has typically monopolized, specifically the exercise of legitimate, lethal violence. I conclude the article by suggesting four characteristics (historically precarious state monopoly on sovereign power; legality of civilian use of guns; preponderance of criminal guns; and US influence) that may encourage demand for guns in high-crime societies.

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)0.002
metaresearch head score (Gemma)0.003
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0050.027
Scholarly communication0.0060.006
Open science0.0000.005
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0030.000

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 head0.057
GPT teacher head0.377
Teacher spread0.320 · 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

Citations73
Published2013
Admission routes2
Has abstractyes

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