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Record W2141247011 · doi:10.1037/a0025661

Artifacts and natural kinds: Children's judgments about whether objects are owned.

2011· article· en· W2141247011 on OpenAlexafffund
Karen R. Neary, Julia W. Van de Vondervoort, Ori Friedman

Bibliographic record

VenueDevelopmental Psychology · 2011
Typearticle
Languageen
FieldNeuroscience
TopicPsychology of Moral and Emotional Judgment
Canadian institutionsUniversity of Waterloo
FundersSocial Sciences and Humanities Research Council of CanadaUniversity of Sheffield
KeywordsPsychologyNatural (archaeology)Object (grammar)Contrast (vision)Social psychologyCognitive psychologyCognitive developmentDevelopmental psychologyCognitionArtificial intelligenceComputer science

Abstract

fetched live from OpenAlex

People's behavior in relation to objects depends on whether they are owned. But how do people judge whether objects are owned? We propose that people expect human-made objects (artifacts) to be more likely to be owned than naturally occurring objects (natural kinds), and we examine the development of these expectations in young children. Experiment 1 found that when shown pictures of familiar kinds of objects, 3-year-olds expected artifacts to be owned and inanimate natural kinds to be non-owned. In Experiments 2A and 2B, 3-6-year-olds likewise had different expectations about the ownership of unfamiliar artifacts and natural kinds. Children at all ages viewed unfamiliar natural kinds as non-owned, but children younger than 6 years of age only endorsed artifacts as owned at chance rates. In Experiment 3, children saw the same pictures but were also told whether objects were human-made. With this information provided, even 3-year-olds viewed unfamiliar artifacts as owned. Finally, in Experiment 4, 4- and 5-year-olds chose unfamiliar artifacts over natural kinds when judging which object in a pair belongs to a person, but not when judging which the person prefers. These experiments provide first evidence about how children judge whether objects are owned. In contrast to claims that children think about natural kinds as being similar to artifacts, the current findings reveal that children have differing expectations about whether they are owned.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.275
Threshold uncertainty score1.000

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)0.0010.001

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 head0.285
Teacher spread0.183 · 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.

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

Citations53
Published2011
Admission routes2
Has abstractyes

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