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Record W3217085909 · doi:10.1080/17440572.2021.1998772

Violence brokers and super-spreaders: how organised crime transformed the structure of Chicago violence during Prohibition

2021· article· en· W3217085909 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueGlobal Crime · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicCrime, Illicit Activities, and Governance
Canadian institutionsUniversity of Toronto
FundersNational Science Foundation
KeywordsVictimisationCriminologyOrganised crimeViolent crimeGun violenceComputer securityPolitical scienceSociologyPoison controlHuman factors and ergonomicsComputer scienceMedical emergencyMedicine

Abstract

fetched live from OpenAlex

The rise of organised crime changed Chicago violence structurally by creating networks of rivalries and conflicts wherein violence ricocheted. This study examines the organised crime violence network during Prohibition by analysing ‘violence brokers’ – individuals who committed multiple violence acts that linked separate violent events into a connected violence network. We analyse the two-mode violence network from the Capone Database, a relational database on early 1900s Chicago organised crime. Across 276 violent incidents attributed to organised crime were 334 suspected perpetrators of violence. We find that 20% of suspects were violence brokers, and nine brokers were violence super-spreaders linking the majority of suspects. We also find that violence brokers were in the thick of violence not just as suspects, but also as victims – violence brokers in this network experienced more victimisation than non-brokers. Unknowingly or knowingly, these violence brokers wove together a network, attack-by-attack, that transformed violence in Chicago.

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.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.297
Threshold uncertainty score0.632

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
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 head0.012
GPT teacher head0.256
Teacher spread0.244 · 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