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Record W1986742968 · doi:10.1111/disa.12057

The impact of a natural disaster on altruistic behaviour and crime

2014· article· en· W1986742968 on OpenAlexaboutno aff
Frédéric Lemieux

Bibliographic record

VenueDisasters · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicCrime Patterns and Interventions
Canadian institutionsnot available
Fundersnot available
KeywordsAltruism (biology)BlackoutSolidarityProperty crimeIntervention (counseling)Natural disasterPoison controlDemographic economicsEconomicsCriminologyPolitical scienceSocial psychologyPsychologyViolent crimeGeographyMedical emergencyMedicinePsychiatry

Abstract

fetched live from OpenAlex

Institutional altruism in the form of a public-sector intervention and support for victims and social altruism generated by mutual aid and solidarity among citizens constitute a coming together in a crisis. This coming together and mutual support precipitate a decrease in crime rates during such an event. This paper presents an analysis of daily fluctuations in crime during the prolonged ice storms in Quebec, Canada, in January 1998 that provoked an electrical blackout. Of particular interest are the principal crisis-related influences on daily crime patterns. A first series of analyses examines the impact of altruistic public-sector mobilisation on crime. A significant decline in property crime rates was noticed when cheques were distributed to crisis victims in financial need in Montérégie, and hence they were attributable to public intervention (institutional altruism). Moreover, the rate of social altruism (financial donations), which was more substantial in adjoining rather than distant regions, was inversely proportional to crime rates.

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.102
Threshold uncertainty score0.203

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.021
GPT teacher head0.371
Teacher spread0.350 · 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 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

Citations49
Published2014
Admission routes1
Has abstractyes

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