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Record W4361001457 · doi:10.1037/dev0001531

Preliminary evidence for progressions in ownership reasoning over the preschool period.

2023· article· en· W4361001457 on OpenAlexaff
Shaylene E. Nancekivell, Natalie S. Davidson, Nicholaus S. Noles, Susan A. Gelman

Bibliographic record

VenueDevelopmental Psychology · 2023
Typearticle
Languageen
FieldPsychology
TopicChild and Animal Learning Development
Canadian institutionsUniversity of Manitoba
FundersEunice Kennedy Shriver National Institute of Child Health and Human DevelopmentNational Institute of Child Health and Human Development
KeywordsPsycINFOPsychologyCognitive developmentDevelopmental psychologyCognitionChild developmentExploratory researchTest (biology)Social psychologyCognitive psychologySocial science

Abstract

fetched live from OpenAlex

= 4.50-5.00, living in Michigan in the United States. We use a battery of four established ownership tasks that tested different aspects of children's ownership thinking. A Guttman test revealed a reliable sequence that explained 81.9% of children's performance. Namely, we discovered that identifying familiar owned objects emerged first, control of permission as a cue to ownership second, understanding ownership transfers third, and the tracking of sets of identical objects last. This ordering suggests two foundational ownership abilities on which more complex reasoning may be built: the ability to include information about familiar owners in children's mental models of objects and recognizing that control is central to ownership. The observed progression is an important first step toward developing a formal ownership scale. This study paves the way for mapping the conceptual and information-processing demands (e.g., executive functioning, memory) that likely underlie change in ownership thinking across childhood. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.266
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.102
GPT teacher head0.412
Teacher spread0.310 · 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; both teacher heads agree on what is shown here.

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

Citations3
Published2023
Admission routes1
Has abstractyes

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