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Record W3147388529 · doi:10.3386/w9632

Teen Births Keep American Crime High

2003· report· en· W3147388529 on OpenAlexaff
Jennifer Hunt

Bibliographic record

VenueNational Bureau of Economic Research · 2003
Typereport
Languageen
FieldSocial Sciences
TopicHomicide, Infanticide, and Child Abuse
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsCriminologyActuarial sciencePsychologyBusiness

Abstract

fetched live from OpenAlex

The United States has a teenage birth rate that is high relative to that of other developed countries, and falling more slowly. Children of teenagers may experience difficult childhoods and hence be more likely to commit crimes subsequently. I assess to what extent lagged teen birth rates can explain why the United States had the highest developed country crime rates in the 1980s, and why US rates subsequently fell so much. For this purpose, I use internationally comparable crime rates measured from the 1989-2000 International Crime Victims Surveys. I find that an increase in the share of young people born to a teen mother increases the assault rate. The type of assault affected is perpetrated by unarmed lone assailants known to the victim by name, particularly at home or at work, and is not reported to the police. The pattern of teen births in the United States explains n30% of the relative fall in assaults by assailants known to the victim, but more than explains the 1980s gap with the rest of the world. I also present evidence on larceny and burglary.

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 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)0.009
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.920
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0090.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0010.002
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.298
GPT teacher head0.544
Teacher spread0.247 · 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 teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreOther

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

Citations7
Published2003
Admission routes1
Has abstractyes

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