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Record W1859985561 · doi:10.1177/0891243214554799

Mourning Mayberry

2015· article· en· W1859985561 on OpenAlexaff
Jennifer Carlson

Bibliographic record

VenueGender & Society · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicCrime, Deviance, and Social Control
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMasculinityIdeologyGender studiesContext (archaeology)NarrativeAppropriationSociologySocioeconomic statusFemicidePolitical scienceCriminologyPoison controlSuicide preventionGeographyDemographyDomestic violencePopulationPolitics

Abstract

fetched live from OpenAlex

This study uses in-depth interviews and participant observation with gun carriers in Michigan to examine how socioeconomic decline shapes the appropriation of guns by men of diverse class and race backgrounds. Gun carriers nostalgically referenced the decline of Mayberry America—a version of America characterized by the stable employment of male breadwinners and low crime rates. While men of color and poor and working-class men bear the material brunt of these transformations, this narrative of decline impacts how both privileged and marginalized men think of themselves as men because of the ideological centrality of breadwinning to American masculinity. Using Young’s (2003) “masculine protectionism” framework, I argue that against this backdrop of decline, men use guns not simply to instrumentally address the threat of crime but also to negotiate their own position within a context of socioeconomic decline by emphasizing their role as protector.

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)0.001
metaresearch head score (Gemma)0.002
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 score0.022
Threshold uncertainty score0.043

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0080.005
Scholarly communication0.0020.002
Open science0.0010.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.000

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 head0.129
GPT teacher head0.358
Teacher spread0.229 · 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

Citations94
Published2015
Admission routes1
Has abstractyes

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