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Record W1586655475

From Infant Attachment Security to Mother-Child Emotion Dialogues: Understanding Emotion Communication in the Early Years

2010· article· en· W1586655475 on OpenAlexafffund
Celia Hsiao, Greg Moran, Nina Koren‐Karie

Bibliographic record

VenueScholarship@Western (Western University) · 2010
Typearticle
Languageen
FieldPsychology
TopicChild and Adolescent Psychosocial and Emotional Development
Canadian institutionsWestern University
FundersHealth Canada
KeywordsPsychologyDevelopmental psychologyEmotion workAttachment theorySocial psychology
DOInot available

Abstract

fetched live from OpenAlex

Researchers have become increasingly interested in understanding the developmental trajectory of attachment security from the preverbal infancy period to later verbal stages. One approach that has received much theoretical and empirical support is to examine the capacity of children and their mothers to work together in co-constructing narratives around personal and emotional events(Bretherton, 1990; Oppenheim, Koren-Karie & Sagi-Schwartz, 2007). It has been suggested (Oppenheim & Koren-Karie, 2009) that open communication between a secure dyad during infancy, involving primarily non-verbal signals, provides the basis for an open and fluid communication style in the preschool years. The secure child feels confident in exploring and sharing his thoughts and feelings with his mother because their earlier joint experiences have assured him that she will be emotionally available, supportive and accepting.

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)0.003
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0010.005
Scholarly communication0.0050.005
Open science0.0010.003
Research integrity0.0010.002
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 head0.095
GPT teacher head0.327
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 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

Citations2
Published2010
Admission routes2
Has abstractyes

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