MétaCan
Menu
Back to cohort
Record W4413378847 · doi:10.31234/osf.io/rwp23_v2

Infant Geometry

2025· preprint· en· W4413378847 on OpenAlexaff
Moira Rose Dillon

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEngineering
TopicSpatial Cognition and Navigation
Canadian institutionsYork University
FundersAssociation for Psychological ScienceNational Science Foundation
KeywordsGeometryAbstractionCategorizationSet (abstract data type)Transformation geometryComputer scienceObject (grammar)Artificial intelligenceMathematicsEpistemologyPhilosophyProgramming language

Abstract

fetched live from OpenAlex

Geometry is at the foundation of many of humanity’s greatest cultural achievements, from art and architecture to science and technology. This chapter explores the origins of humans’ unique capacity for geometry by describing the early emerging geometric sensitivities of human infants. Initially, infants show limited sensitivities to the geometry of 2D visual forms, discriminating the relative lengths of a form’s parts but not the angles at which those parts meet, consistent with the hypothesis that infants differentiate forms by their shape skeletons. Nevertheless, infants’ sensitivities to the geometry of 2D visual forms may become increasingly abstract with their acquisition of natural language, paralleling the increased geometric abstraction that underlies toddlers’ developing 3D object recognition and categorization. Such development may ultimately support informal learning of the foundational geometric building blocks of formal geometry, like those found in Euclid’s Elements, with concepts like parallelism, perpendicularity, and angle, and may thus set the stage for formal learning of geometry in school.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.931
Threshold uncertainty score0.681

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 integrity0.0000.000
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 head0.011
GPT teacher head0.243
Teacher spread0.232 · 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 designSimulation or modeling
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

Citations0
Published2025
Admission routes1
Has abstractyes

Explore more

Same topicSpatial Cognition and NavigationFrench-language works237,207