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The Genetic and Environmental Basis of the Relationship Between Schizotypy and Personality

2005· article· en· W1998248576 on OpenAlexaff
Kerry L. Jang, Todd S. Woodward, Donna J. Lang, William G. Honer, W. John Livesley

Bibliographic record

VenueThe Journal of Nervous and Mental Disease · 2005
Typearticle
Languageen
FieldPsychology
TopicPersonality Disorders and Psychopathology
Canadian institutionsSimon Fraser UniversityUniversity of British Columbia
Fundersnot available
KeywordsSchizotypySchizotypal personality disorderPersonalityPsychologySchizophrenia (object-oriented programming)PsychosisDevelopmental psychologyPopulationBig Five personality traitsTwin studyClinical psychologyPsychiatrySocial psychologyGeneticsHeritabilityMedicineBiology

Abstract

fetched live from OpenAlex

The clinical phenotype commonly referred to as schizotypy is used in two different ways in psychiatric practice. One usage emphasizes psychosis-proneness where schizotypy is considered part of the schizophrenia spectrum. The other emphasizes personality aberrations and is classed as a personality disorder. The present study provides evidence that schizotypy is a unitary construct and that features like schizophrenia and personality share a common genetic basis. A sample of 102 monozygotic and 90 dizygotic general population twin pairs completed measures of psychosis-proneness and traits delineating personality disorder. Multivariate genetic analyses showed that the observed relationship between psychotic and personality features is caused almost entirely by common genetic factors. Environmental factors appear to be unique to each measure. On the basis of these findings, it is suggested that the environment mediates change in personality function to psychosis as proposed by Meehl's original concept of schizotaxia.

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)0.003
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 score0.003
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.026
GPT teacher head0.290
Teacher spread0.264 · 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

Citations39
Published2005
Admission routes1
Has abstractyes

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