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Record W2009524386 · doi:10.1108/20093821211210495

Pre‐offense characteristics of nineteenth‐century American parricide offenders: an archival exploration

2012· article· en· W2009524386 on OpenAlexaff
Phillip Shon, Shannon M. Barton

Bibliographic record

VenueJournal of Criminal Psychology · 2012
Typearticle
Languageen
FieldSocial Sciences
TopicCrime Patterns and Interventions
Canadian institutionsOntario Tech University
Fundersnot available
KeywordsPsychologyCriminologyJuvenile delinquencyOriginalityNewspaperValue (mathematics)JuvenileIdentification (biology)Developmental psychologySocial psychologySociologyMedia studies

Abstract

fetched live from OpenAlex

Purpose Previous criminological research has examined the causes and correlates of violent juvenile offending, but failed to explore the developmental taxonomies of crime throughout history. Theoretically, developmental trajectories of offending (i.e. life‐course persistent and adolescence‐limited offenders) should be identifiable irrespective of time and place. This study aims to examine the pre‐offense characteristics of nineteenth‐century American parricide offenders. Design/methodology/approach Using archival records of two major newspapers (New York Times, Chicago Tribune), the study examines 220 offenders who committed attempted and completed parricides during the latter half of the nineteenth century (1852‐1999). Findings Results reveal that a small group of adult parricide offenders displayed antisocial tendencies at an early age that persisted into adulthood. These findings are consistent with the developmental literature, thus providing support for identification of pre‐offense characteristics of parricide offenders across historical periods. Originality/value The findings reported in this paper are of value to psychologists, historians, and criminologists, for they illuminate the similarities in predictors related to violent behaviors in a small subsection of adult offenders across two centuries.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.696
Threshold uncertainty score0.428

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.142
GPT teacher head0.456
Teacher spread0.314 · 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.

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

Citations26
Published2012
Admission routes1
Has abstractyes

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