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Record W6990616947

Drug Violence, Fear of Crime and the Transformation of Everyday Life in the Mexican Metropolis

2016· dissertation· en· W6990616947 on OpenAlexfundno aff

Bibliographic record

VenueeScholarship (California Digital Library) · 2016
Typedissertation
Languageen
FieldSocial Sciences
TopicSociology and Norbert Elias
Canadian institutionsnot available
FundersInternational Development Research CentreJohn Simon Guggenheim Memorial FoundationUniversity of California Institute for Mexico and the United StatesAmerican Association of University Women
KeywordsEveryday lifeState (computer science)EthnographyMetropolitan areaSeclusionNexus (standard)PoliticsFear of crimeSocial transformationSociological imagination
DOInot available

Abstract

fetched live from OpenAlex

This dissertation brings sharp social theory, deep history and precise ethnography to illumine the nexus of social and urban structure, human emotions, and power. I draw on Norbert Elias, Emile Durkheim, Marcel Mauss, among other social theorists and historians, to counter dominant views of fear in the social sciences as a sole destroyer of the social fabric with evidence of how and why fear both tears and tightens the social fabric, both destroys and fosters solidarity. Yet with the exception of a few spaces of hope where families of victims of forced disappearances organized to demand justice from the state, this “tightening” of the social fabric did not transcend but rather exacerbated socio-spatial divides. I draw on comparative urban sociology by Teresa Caldeira, Gerald Suttles and Loïc Wacquant to trace the revamping of San Pedro, a suburb of the Monterrey Metropolitan Area and one of the wealthiest municipalities in Mexico, as an emerging state within a state for the upper class—an example of a new pattern of urban seclusion taking form in Latin America. This dissertation contributes to the sociology of everyday life in the city and the political sociology of fear and violence by providing a rare case study of cross-class responses to gruesome violence.

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)0.001
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 score0.120
Threshold uncertainty score0.481

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.002
Open science0.0010.000
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 head0.012
GPT teacher head0.262
Teacher spread0.250 · 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

Citations0
Published2016
Admission routes1
Has abstractyes

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