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Record W1986781713 · doi:10.1177/108876702237344

Similarities in Homicide Trends in the United States and Canada

2002· article· en· W1986781713 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

VenueHomicide Studies · 2002
Typearticle
Languageen
FieldSocial Sciences
TopicCrime Patterns and Interventions
Canadian institutionsUniversity of TorontoUniversity of Guelph
Fundersnot available
KeywordsHomicideDemographicsDemographyPoison controlInjury preventionSuicide preventionHuman factors and ergonomicsGeographyOccupational safety and healthMedicineDemographic economicsEconomicsMedical emergencySociology

Abstract

fetched live from OpenAlex

The decrease in the overall homicide rate in the United States during the latter 1990s has been explained in terms of changes in various factors such as the availability of guns, crack markets, and demographics. Although these are all plausible explanations, they do not explain why Canada has experienced similar declines in homicide rates during that same period. Homicides in Canada are qualitatively different from homicides in the United States, and thus changes in gun availability or crack markets are likely not behind the decrease in Canada’s homicide rate. However, changes in demographics might be one explanation behind Canada’s decreasing homicide rate. Analyses revealed that as in U.S. research findings, changes in demographics appear to account for roughly 14% of Canada’s decreasing homicide rate. Thus, although the homicides are qualitatively different from one another, demographics appear to account for similarly small proportions of the change in homicide rates in both countries.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.255
Threshold uncertainty score0.272

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 studies0.0000.000
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 head0.103
GPT teacher head0.359
Teacher spread0.255 · 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