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Record W2742320783 · doi:10.5539/jedp.v7n2p68

Accelerated Cognitive Development—Piaget’s Conservation Concept

2017· article· en· W2742320783 on OpenAlexvenueno aff
Nobuki Watanabe

Bibliographic record

VenueJournal of Educational and Developmental Psychology · 2017
Typearticle
Languageen
FieldPsychology
TopicChild and Animal Learning Development
Canadian institutionsnot available
FundersJapan Society for the Promotion of Science
KeywordsCognitive developmentPiaget's theory of cognitive developmentPsychologyCognitionDevelopmental psychologyDevelopmental stageCognitive psychologySocial psychology

Abstract

fetched live from OpenAlex

Piaget’s ideas have significantly influenced education and psychology, particularly the concept of conservation, which he had proposed as being acquired during the concrete operational stage. However, research conducted after Piaget found that children under the age of 6 are unable to understand his concept of conservation. However, more recent studies have found that three-year-olds may be able to acquire this concept, even when tested using the same tasks. But, this study addresses the issues of “fixity” and “reliability” for the concept of conservation. Then, the robustness (fixity and reliability) of Piaget’s concept of conservation (numbers/length) was examined by observing a four-year-old child who demonstrated the possible acquisition of this concept at the age 3, in this study. It was found that the child was able to robustly maintain the concept. Therefore, the study shows the possibility of accelerated cognitive development for Piaget’s concept of conservation. The reason may be that younger children have higher intelligence than those in previous generations. And, the grounds may be that of the influence of gene-environment interaction.

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)0.006
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 score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.003
Scholarly communication0.0010.002
Open science0.0010.001
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 head0.078
GPT teacher head0.395
Teacher spread0.317 · 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

Citations11
Published2017
Admission routes1
Has abstractyes

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