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Sibling Relationships in Adolescence

2020· other· en· W2999951429 on OpenAlexaff
Nina Howe, Ryan J. Persram, Catherine Bergeron

Bibliographic record

VenueThe Encyclopedia of Child and Adolescent Development · 2020
Typeother
Languageen
FieldPsychology
TopicChild and Adolescent Psychosocial and Emotional Development
Canadian institutionsMcGill UniversityConcordia University
Fundersnot available
KeywordsSiblingPsychologyProsocial behaviorDevelopmental psychologySibling relationshipPsychological interventionSocializationPerspective (graphical)Attachment theoryAggressionClosenessPsychopathologyJuvenile delinquencySocial psychologyClinical psychology

Abstract

fetched live from OpenAlex

Across the globe, the vast majority of adolescents have siblings, who play an important socialization role in one another's development, given their long and mutually constructed history and strong attachment to one another. Critical theory for understanding the role of adolescent siblings in the family and in each other's lives is discussed from the perspective of relationships, family systems, social learning, psychodynamic, and behavioral–genetic theories. These theories provide a framework for the discussion of characteristics of the sibling relationship, which includes a focus on structural features, cultural differences, and relationship quality as key factors. Associations between sibling relationship quality and developmental outcomes include both positive and negative outcomes. Positive outcomes focus on prosocial behaviors such as kindness, sympathy, and emotional self‐regulation, whereas negative outcomes include aggression, bullying, and engaging in at‐risk behaviors such as delinquency and substance abuse. In addition, the association between relationship quality and psychopathology is covered, and also interventions for dealing with problematic sibling relationships. Finally, the effects of parents on sibling relations and different types of sibling relationships (i.e., children with a sibling with special needs, step‐ and half‐siblings, singletons) are also reviewed.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.222
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.022
GPT teacher head0.254
Teacher spread0.232 · 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.

Study designNot applicable
Domainnot available
GenreOther

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

Citations12
Published2020
Admission routes1
Has abstractyes

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