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Record W2059622553 · doi:10.1037/0012-1649.42.2.207

Social perspective-taking skills in maltreated children and adolescents.

2006· article· en· W2059622553 on OpenAlexaff
Jacob A. Burack, Tara Flanagan, Terry Peled, Hazel M. Sutton, Catherine Zygmuntowicz, Jody Todd Manly

Bibliographic record

VenueDevelopmental Psychology · 2006
Typearticle
Languageen
FieldPsychology
TopicChild and Adolescent Psychosocial and Emotional Development
Canadian institutionsMcGill University
Fundersnot available
KeywordsPsychologyEgocentrismDevelopmental psychologyPerspective (graphical)Perspective-takingInterpersonal relationshipFeelingChild neglectInterpersonal communicationChild abusePoison controlInjury preventionSocial psychologyEmpathy

Abstract

fetched live from OpenAlex

The primary goal of this study was to assess the ability of maltreated school-age children and adolescents to understand the thoughts, feelings, and points of view of others. Level of egocentrism and social perspective-taking coordination were assessed in a group of 49 maltreated and 49 demographically matched nonmaltreated children. Twenty-six elementary and 23 high school students in each group were individually interviewed and their responses to hypothetical interpersonal situations coded for egocentricity and level of perspective-taking ability. The findings revealed that maltreated children and adolescents were more egocentric and delayed in their social perspective-taking development than their nonmaltreated peers and that they reported lower levels of global self-worth. However, differences within the group of maltreated children and adolescents emerged with regard to negotiating novel relationships, as those with fewer internalizing or externalizing symptoms exhibited better skills in this area than their peers who displayed more symptoms.

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)0.000
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.000
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 head0.013
GPT teacher head0.308
Teacher spread0.295 · 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

Citations105
Published2006
Admission routes1
Has abstractyes

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