Artifacts and natural kinds: Children's judgments about whether objects are owned.
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".