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Record W2526436464 · doi:10.1111/sode.12212

Different ways of knowing a child and their relations to mother‐reported autonomy support

2016· article· en· W2526436464 on OpenAlexafffund
Geneviève A. Mageau, Amanda Sherman, Joan E. Grusec, Richard Koestner, Julien S. Bureau

Bibliographic record

VenueSocial Development · 2016
Typearticle
Languageen
FieldPsychology
TopicChild and Adolescent Psychosocial and Emotional Development
Canadian institutionsMcGill UniversityUniversity of TorontoUniversité de Montréal
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsPsychologyAutonomySocializationPerspective (graphical)Developmental psychologyFeelingSocial psychologyDistressClinical psychology

Abstract

fetched live from OpenAlex

Abstract We considered how different forms of child knowledge (i.e., mothers’ reports of taking their child's perspective, their accurate knowledge in the form of precise predictions of their child's ratings regarding distress/comforting and compliance/discipline situations, and their perceived knowledge) are differentially associated with mother‐reported autonomy support (i.e., providing meaningful rationales, providing choice, and acknowledging feelings; Koestner, Ryan, Bernieri, & Holt, ). Mothers and their children (141 dyads, M = 11 years old at Time 1) participated in a two‐wave longitudinal study with assessments made two years apart. The only form of knowledge that predicted changes in autonomy support was perspective‐taking. Autonomy support, in turn, indirectly predicted changes in distress/comforting accuracy through child‐reported self‐disclosure and directly predicted changes in perceived knowledge. These findings underline the importance of differentiating among forms of child knowledge in the study of socialization processes.

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)0.002
metaresearch head score (Gemma)0.019
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 score0.006
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.019
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.037
GPT teacher head0.266
Teacher spread0.229 · 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

Citations30
Published2016
Admission routes2
Has abstractyes

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