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Record W2119766209 · doi:10.1007/s11194-006-9022-3

Sexual Offenders’ State-of-Mind Regarding Childhood Attachment: A Controlled Investigation

2006· article· en· W2119766209 on OpenAlexaff
Tania Stirpe, Jeffrey Abracen, Lana Stermac, Robin Wilson

Bibliographic record

VenueSexual Abuse · 2006
Typearticle
Languageen
FieldPsychology
TopicAttachment and Relationship Dynamics
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsPsychologyNormativeSexual abuseDevelopmental psychologyAttachment measuresClinical psychologySuicide preventionPoison controlAttachment theoryMedicineMedical emergency

Abstract

fetched live from OpenAlex

Attachment experiences have been regarded as significant by researchers and clinicians attempting to explain the etiology of sexual offending. Although initial studies have revealed some promising evidence, there are a number of theoretical and methodological problems with this preliminary body of work. While addressing these limitations, the goal of the present study was to investigate state-of-mind regarding childhood attachment among subtypes of sexual offenders, comparing them to both a sample of nonsexual offenders and to the documented patterns of nonoffenders. Sixty-one sexual offenders (extrafamilial child molesters, incest offenders, and rapists) and 40 nonsexual offenders (violent and nonviolent) were administered the "Adult Attachment Interview." Results indicated that the majority of sexual offenders were insecure, representing a marked difference from normative samples. Although insecurity of attachment was common to all groups of offenders, there were important differences in regard to the specific type of insecurity. Most notable were the child molesters, who were significantly more likely to be Preoccupied. Rapists, violent offenders, and, to a lesser degree, incest offenders were more likely to be Dismissing. Although still most likely to be Dismissing, nonviolent offenders were comparatively more Secure.

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)0.000
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 score0.040
Threshold uncertainty score0.881

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 head0.024
GPT teacher head0.325
Teacher spread0.301 · 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

Citations38
Published2006
Admission routes1
Has abstractyes

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