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Record W4312915452 · doi:10.31389/jied.134

The Pandemic and Organized Crime in Urban Latin America: New Sovereignty Arrangements or Business as Usual?

2022· article· en· W4312915452 on OpenAlexaff
Diane E. Davis, Tina Hilgers

Bibliographic record

VenueJournal of Illicit Economies and Development · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicCrime, Illicit Activities, and Governance
Canadian institutionsConcordia University
Fundersnot available
KeywordsSovereigntyLegitimacyPolitical scienceLatin AmericansContext (archaeology)Corporate governanceOrganised crimeState (computer science)Political economyPandemicDemocracySociologyPublic administrationLawCoronavirus disease 2019 (COVID-19)PoliticsGeographyBusiness

Abstract

fetched live from OpenAlex

Using a focus on the ways that Covid-19 has impacted everyday life in urban Latin America, this article examines the shifting activities of organized criminal groups in the context of a global pandemic. Using grounded ethnographic fieldwork drawn from Brazil, it asks whether a health crisis with direct life and death consequences has empowered illicit actors, and by so doing changed longstanding relationships between illicit actors and citizens on one hand, and/or illicit actors and local authorities on the other. Its larger aim is to understand whether and how the global pandemic has impacted governance by producing new scalar and sovereignty tensions between state and non-state actors at the scale of the city, and with what implications for the legitimacy of national authorities and democratic governance more generally.

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)0.004
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 score0.025
Threshold uncertainty score0.049

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.016
Scholarly communication0.0050.004
Open science0.0010.005
Research integrity0.0010.002
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 head0.032
GPT teacher head0.271
Teacher spread0.239 · 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

Citations10
Published2022
Admission routes1
Has abstractyes

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