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Record W3015025532 · doi:10.33526/ejks.20201902.195

Sources of Conflict: A Comparative Synthesis of American and Korean Parricides

2020· article· en· W3015025532 on OpenAlexaff
Phillip Shon

Bibliographic record

VenueEuropean Journal of Korean Studies · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicHomicide, Infanticide, and Child Abuse
Canadian institutionsOntario Tech University
Fundersnot available
KeywordsCriminologyAsidePsychologyNominative caseMental illnessIdentity (music)Social psychologyDevelopmental psychologyMental healthPsychiatryLinguistics

Abstract

fetched live from OpenAlex

Despite the nominative classification of parricides based on the victim–offender relationship, parricide bears the offense characteristics of many crimes. In prior works, the killing of parents has been framed as a violent reaction of severely abused children against their tormentors, or as the identity demarcating actions of adult sons suffering from mental illness. Aside from these two primary discourses, the reasons parents and their offspring become mired in conflicts across various life stages of both participants have been neglected from the literature. A more recent theoretical framework examines parricides and their sources of conflict across the life course of the victims and offenders. This paper synthesizes the sources of conflict in parricides in nineteen-century America and twentiethcentury South Korea by comparing the similarities and differences in offense characteristics. I argue that parricides in the two countries can be differentiated based on the differences in history and culture.

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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.005
Science and technology studies0.0040.002
Scholarly communication0.0040.004
Open science0.0010.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.077
GPT teacher head0.325
Teacher spread0.248 · 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 designQualitative
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

Citations0
Published2020
Admission routes1
Has abstractyes

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