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Record W3080130437 · doi:10.1177/0003122420948510

Exogenous Shocks, the Criminal Elite, and Increasing Gender Inequality in Chicago Organized Crime

2020· article· en· W3080130437 on OpenAlexaff
Chris Smith

Bibliographic record

VenueAmerican Sociological Review · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicCrime, Illicit Activities, and Governance
Canadian institutionsUniversity of Toronto
FundersNational Institute of JusticeNational Science Foundation
KeywordsOrganised crimeInequalityRestructuringEliteHoarding (animal behavior)CriminologyPower (physics)EnforcementPolitical scienceEconomicsSociologyLawPolitics

Abstract

fetched live from OpenAlex

Criminal organizations, like legitimate organizations, adapt to shifts in markets, competition, regulations, and enforcement. Exogenous shocks can be consequential moments of power consolidation, resource hoarding, and inequality amplification in legitimate organizations, but especially in criminal organizations. This research examines how the exogenous shock of the U.S. prohibition of the production, transportation, and sale of alcohol in 1920 restructured power and inequality in Chicago organized crime. I analyze a unique relational database on organized crime from the early 1900s via a criminal network that tripled in size and centralized during Prohibition. Before Prohibition, Chicago organized crime was small, decentralized, and somewhat inclusive of women at the margins. However, during Prohibition, the organized crime network grew, consolidated the organizational elites, and left out the most vulnerable participants from the most profitable opportunities. This historical case illuminates how profits and organizational restructuring outside of (or in response to) regulatory environments can displace people at the margins.

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)0.002
metaresearch head score (Gemma)0.006
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.566
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.002
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 head0.116
GPT teacher head0.368
Teacher spread0.252 · 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 designObservational
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

Citations18
Published2020
Admission routes1
Has abstractyes

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