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Record W3012380817 · doi:10.1093/socpro/spaa001

The Impact of an Experimental Guaranteed Income on Crime and Violence

2020· article· en· W3012380817 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.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueSocial Problems · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicCrime Patterns and Interventions
Canadian institutionsWestern University
FundersNational Science Foundation
KeywordsProperty crimePaymentContext (archaeology)CashViolent crimeCensusCriminologyProperty (philosophy)Demographic economicsEconomicsGeographySociologyDemographyFinance

Abstract

fetched live from OpenAlex

Abstract Would unconditional cash payments reduce crime and violence? This paper examines data on crime and violence in the context of an understudied social experiment from the late 1970s called the Manitoba Basic Annual Income Experiment, or Mincome. We combine town-level crime statistics for all medium-sized Canadian Prairie towns with town-level socio-demographic data from the census to study how an experimental guaranteed income affected both violent crime and total crime. We find a significant negative relationship between Mincome and both outcomes. We also decompose total crime and analyze its main components, property crime and “other” crime, and find a significant negative relationship between Mincome and property crime. While the impact on property crime is theoretically straightforward, we close by speculating on the mechanisms that might link the availability of guaranteed annual income payments to a decline in violence, focusing on the mechanisms that shape patterns of inter-partner 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.

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: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.531
Threshold uncertainty score0.754

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 studies0.0010.000
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 head0.063
GPT teacher head0.400
Teacher spread0.337 · 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