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Children's Self‐Regulation and Executive Control

2014· other· en· W1594521212 on OpenAlexaff
Caron A. C. Clark, Miriam M. Martinez, Jennifer Mize Nelson, Sandra A. Wiebe, Kimberly Andrews Espy

Bibliographic record

VenueWell Being · 2014
Typeother
Languageen
FieldMathematics
TopicCognitive and developmental aspects of mathematical skills
Canadian institutionsUniversity of Alberta
FundersNational Institutes of Health
KeywordsSocioemotional selectivity theoryDevelopmental psychologyFlexibility (engineering)PsychologyControl (management)Executive functionsSelf-controlTask (project management)Early childhoodInhibitory controlWorking memoryCognitive psychologyCognitive flexibilityCognitionComputer scienceNeuroscienceArtificial intelligence

Abstract

fetched live from OpenAlex

Self‐regulation is essential for adaptive behavior in everyday contexts that become increasingly elaborated and complex through childhood and adolescence. Here, we review literature and discuss our own findings regarding the development of executive control (EC) skills that support gains in self‐regulation over the preschool years. Children's accuracy on executive inhibitory control, working memory, and flexibility tasks increases dramatically between 3 and 4 years of age, paralleling both quantitative and qualitative changes in neural volume and connectivity. Levels of proficiency on theseECmeasures are strongly tied to children's sociofamilial backgrounds, and particularly to family financial resources that facilitate access to learning materials and supports. Moreover, our data indicate that children's level of performance onECtasks administered during preschool mediates the relation of family financial resources to children's mathematical achievement in early kindergarten. Collectively, findings indicate that the acquisition ofECrepresents a critical developmental task of early childhood and provides a platform for wellbeing across academic and socioemotional domains.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.004
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
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 head0.006
GPT teacher head0.232
Teacher spread0.226 · 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 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

Citations1
Published2014
Admission routes1
Has abstractyes

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