MétaCan
Menu
Back to cohort

Understanding Interest in the Second Year of Life

2010· article· en· W1601401495 on OpenAlexfundno aff
Amy C. MacPherson, Chris Moore

Bibliographic record

VenueInfancy · 2010
Typearticle
Languageen
FieldPsychology
TopicChild and Animal Learning Development
Canadian institutionsnot available
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsPsychologyObject (grammar)Context (archaeology)Developmental psychologyDiversity (politics)Object permanenceSocial psychologyCognitive developmentCognitionArtificial intelligenceComputer science

Abstract

fetched live from OpenAlex

Infants (n = 24, mean age 13 months and n = 24, mean age 19 months) were tested on an extension of the method introduced by Tomasello and Haberl (2003) to examine the understanding of another person's interest in a novel object. Four objects were presented serially. For two objects, infants played with an experimenter. The infant played with one object alone, and the experimenter played with one object alone. Finally, all four objects were presented together, and the experimenter excitedly asked for one without indicating which. Results showed that younger infants tended to chose the object that they had not yet played with, whereas older infants were significantly more likely to choose the object that the experimenter had not yet played with. These results are discussed in the context of research on the development of understanding diversity of simple object-directed attitudes in the second year of life.

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)0.005
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 score0.005
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
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 head0.112
GPT teacher head0.314
Teacher spread0.202 · 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

Citations43
Published2010
Admission routes1
Has abstractyes

Explore more

Same venueInfancySame topicChild and Animal Learning DevelopmentFrench-language works237,207