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Record W2322758860 · doi:10.1037/a0036971

Mine, yours, no one’s: Children’s understanding of how ownership affects object use.

2014· article· en· W2322758860 on OpenAlexafffund
Shaylene E. Nancekivell, Ori Friedman

Bibliographic record

VenueDevelopmental Psychology · 2014
Typearticle
Languageen
FieldNeuroscience
TopicPsychology of Moral and Emotional Judgment
Canadian institutionsUniversity of Waterloo
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsObject (grammar)PsychologySocial psychologyIdentity (music)Developmental psychologyProperty (philosophy)

Abstract

fetched live from OpenAlex

Two experiments examined children's understanding of how ownership affects object use. In Experiment 1, 84 children age 4-6 were asked what a person was allowed to do with a human-made object that either belonged to the person or belonged to someone else. In Experiment 2, 44 children age 4-5 were asked what a person was allowed to do with a natural object that belonged to the person, belonged to someone else, or belonged to no one. Children were permitted to provide as many responses as they wished. Children's responses reflected an appreciation that ownership affects a wide range of object uses, including harmless object use. Children's responses also reflected an appreciation that owner's rights should be upheld regardless of whether the owner's identity is known, or whether the owner is nearby. Moreover, children's responses suggest that they view ownership as restricting nonowners from using property, rather than affecting use by entitling owners.

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)0.002
metaresearch head score (Gemma)0.007
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score0.027

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.004
Scholarly communication0.0020.003
Open science0.0000.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.193
GPT teacher head0.298
Teacher spread0.104 · 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

Citations34
Published2014
Admission routes2
Has abstractyes

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