MétaCan
Menu
Back to cohort
Record W2728998806 · doi:10.1080/10986065.2017.1328634

Gender Differences in Toddlers’ Visual-Spatial Skills

2017· article· en· W2728998806 on OpenAlexafffund
Donna Kotsopoulos, Joanna Zambrzycka, Samantha Makosz

Bibliographic record

VenueMathematical Thinking and Learning · 2017
Typearticle
Languageen
FieldEngineering
TopicSpatial Cognition and Navigation
Canadian institutionsUniversity of TorontoWilfrid Laurier University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMental rotationSpatial abilityPsychologyDevelopmental psychologySpatial cognitionCognitionSpatial memoryVisual perceptionSpatial intelligenceVisual memoryPerceptionWorking memory

Abstract

fetched live from OpenAlex

The purpose of the present study was to determine whether there are visual-spatial gender differences in two-year-olds, to investigate the environmental and cognitive factors that contribute to two-year-olds’ visual-spatial skills, and to explore whether these factors differ for boys and girls. Children (N = 63; Mage = 28.17 months) were assessed on their visual-spatial skills and on measures related to visual-spatial skills: intelligence, quantitative reasoning, working memory, and home spatial activity engagement. Children’s mothers were assessed on mental rotation ability. Results found no difference between boys’ and girls’ visual-spatial skills at age two. Quantitative reasoning contributed the most to girls’ visual-spatial skills. No variables were predictive for the boys, though boys with higher spatial activity frequency had higher visual-spatial skills. The differential predictors have implications for the development and fostering of visual-spatial skills, particularly for girls, who may be at a disadvantage in this area when they are older.

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)0.003
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 score0.008
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.003
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 science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.025
GPT teacher head0.269
Teacher spread0.244 · 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

Citations7
Published2017
Admission routes2
Has abstractyes

Explore more

Same venueMathematical Thinking and LearningSame topicSpatial Cognition and NavigationFrench-language works237,207