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Record W2097152432 · doi:10.1186/2193-7680-2-5

Five tests for a theory of the crime drop

2013· article· en· W2097152432 on OpenAlexafffund
Graham Farrell

Bibliographic record

VenueCrime Science · 2013
Typearticle
Languageen
FieldSocial Sciences
TopicCrime Patterns and Interventions
Canadian institutionsSimon Fraser University
FundersEconomic and Social Research CouncilSimon Fraser UniversityTemple University
KeywordsLegalizationImprisonmentCriminologyGun controlImmigrationAbortionDemographicsEconomicsPolitical scienceComputer securityActuarial scienceDemographic economicsPsychologySociologyLawDemographyComputer science

Abstract

fetched live from OpenAlex

Many studies have sought to explain the major crime declines experienced in most advanced countries. Key hypotheses relate to: lead poisoning; abortion legalization; drug markets; demographics; policing numbers and strategies; imprisonment; strong economies; the death penalty; gun control; gun concealment; immigration; consumer confidence; the civilizing process, and; improved security. This paper outlines five tests that a hypothesis should pass to be considered further. It finds that fourteen of the fifteen hypotheses fail two or more tests. The security hypothesis appears to pass the tests, and thereby pave the way for further research.

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)0.042
metaresearch head score (Gemma)0.243
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.043
Threshold uncertainty score0.221

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0420.243
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.004
Bibliometrics0.0050.003
Science and technology studies0.0020.012
Scholarly communication0.0050.009
Open science0.0050.005
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0430.002

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 head0.071
GPT teacher head0.390
Teacher spread0.319 · 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 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

Citations114
Published2013
Admission routes2
Has abstractyes

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