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Record W4214944129 · doi:10.1038/s41598-022-07542-4

Grit increases strongly in early childhood and is related to parental background

2022· article· en· W4214944129 on OpenAlexfundno aff
Matthias Sutter, Anna Untertrifaller, Claudia Zoller

Bibliographic record

VenueScientific Reports · 2022
Typearticle
Languageen
FieldPsychology
TopicGrit, Self-Efficacy, and Motivation
Canadian institutionsnot available
FundersHealth Technology Assessment internationalUniversität zu KölnDeutsche Forschungsgemeinschaft
KeywordsGritPatienceTask (project management)PsychologyDevelopmental psychologyPsychological interventionCognitionSocial psychologyEconomics

Abstract

fetched live from OpenAlex

Grit has been identified as a very important non-cognitive skill that is positively related to educational achievements and labor market success. Recently, it has also been found to be malleable through interventions in primary schools. Yet, little is still known about its development in early childhood and the influence of family background. We present an experiment with 429 children, aged 3-6 years. We measure the level of grit as children's perseverance in a real effort task and their willingness to challenge themselves successfully with another, more difficult task. Based on a principal component analysis, we find that grit increases strongly with age. Parents' assessment of their child's grit is correlated with the actual behavior of their child. Education of parents plays a role for perseverance. Yet, children's level of patience is unrelated to their level of grit.

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)0.004
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
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.000
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 head0.017
GPT teacher head0.279
Teacher spread0.262 · 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

Citations15
Published2022
Admission routes1
Has abstractyes

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