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Record W2063911596 · doi:10.1177/1476718x13510914

Gesturing about number sense

2014· article· en· W2063911596 on OpenAlexaff
Joanne Lee, Donna Kotsopoulos, Anupreet Tumber, Samantha Makosz

Bibliographic record

VenueJournal of Early Childhood Research · 2014
Typearticle
Languageen
FieldMathematics
TopicCognitive and developmental aspects of mathematical skills
Canadian institutionsWilfrid Laurier University
Fundersnot available
KeywordsGestureSession (web analytics)Cardinality (data modeling)ToddlerPsychologyDevelopmental psychologyCommunicationComputer scienceArtificial intelligence

Abstract

fetched live from OpenAlex

Gestures such as finger counting, pointing, and touching have been found to facilitate mathematical development in preschool and school-aged children. However, little is known about the types of mathematically related gestures used by parent–toddler dyads to facilitate early mathematics learning during the first 3 years of life. A total of 24 children (12 boys and 12 girls) between 18 and 25 months of age and their parents participated in a recorded 30-minute play session at home. After the play session, each child completed a task to ascertain his or her counting ability from one to five. Parents initiated significantly more instances of mathematically related gestures than did the children. In contrast, children responded with more gestures to mathematically related talk than did their parents. The most frequent types of gestures produced are collecting/grouping of items in an array, counting objects while enumerating, tapping/touching, holding up, and pointing at an item. A total of 13 children demonstrated some understanding of the five counting principles except the cardinality principle proposed by Gelman and Gallistel. Our findings suggest that parents use specific types of mathematically related gestures during play with their toddlers.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.368
Threshold uncertainty score0.669

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.001

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 head0.059
GPT teacher head0.379
Teacher spread0.320 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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

Citations5
Published2014
Admission routes1
Has abstractyes

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