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Reorganization in coping behavior at 1½ years: dynamic systems and normative change

2004· article· en· W2090436513 on OpenAlexaff
Marc D. Lewis, Sara Zimmerman, Tom Hollenstein, Alex V. Lamey

Bibliographic record

VenueDevelopmental Science · 2004
Typearticle
Languageen
FieldPsychology
TopicMental Health Research Topics
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsNormativePsychologyCoping (psychology)NoveltyDevelopmental psychologyDistressCognitionClinical psychologySocial psychology

Abstract

fetched live from OpenAlex

By the age of 1 year toddlers demonstrate distinct coping habits for dealing with frustration. However, these habits may be open to change and reorganization at subsequent developmental junctures. We investigated change in coping habits at 18-20 months, a normative age for major advances in social cognition, focusing on the dynamic systems principles of fluctuation and novelty at transitions. Specifically, we asked whether month-to-month fluctuation, novel behavioral habits and real-time variability increased at the age of a normative transition, despite individual differences in the content of behavior. Infants were given frustrating toys while their mothers sat nearby without helping, on monthly visits at 14-25 months (before, during and after the hypothesized transition). State space grids representing patterns of behavioral durations were constructed for each episode and compared over age. As predicted, month-to-month fluctuation in grid patterns increased temporarily between 17 and 20 months, partly independently of a concurrent peak in distress, and new behavioral habits replaced old ones at the same age. Coping habits changed differently for high-and low-distressed toddlers. However, changes in real-time variability did not generally meet our expectations.

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)0.001
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.082
GPT teacher head0.411
Teacher spread0.329 · 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

Citations64
Published2004
Admission routes1
Has abstractyes

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