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Record W7154624971 · doi:10.48448/gwq9-0c20

Children consider costs to owners when reasoning about ownership transgressions

2025· other· W7154624971 on OpenAlexaff
Cognitive Science Society 2025, Alyssa Doerksen, Emilee Haas, Madison Pesowski, Alexis Smith-Flores

Bibliographic record

VenueUnderline Science Inc. · 2025
Typeother
Language
Field
Topic
Canadian institutionsUniversity of the Fraser Valley
Fundersnot available
KeywordsCognitionProperty (philosophy)Action (physics)Property rightsMinor (academic)Cost–benefit analysis

Abstract

fetched live from OpenAlex

Ownership affords different rights and privileges to owners than non-owners. We investigated whether children view transgressions that impose a large or permanent cost to the owner as less acceptable than (1) actions that impose small or temporary costs, and (2) actions that do not impose any costs to owners. Children aged three to eight years (N=72) and adults (N=72) were shown vignettes in which an agent interacts with someone else’s property without permission. Both adults and children judged actions that imposed severe costs to owners as less acceptable than minor transgressions that imposed temporary costs and actions that did not involve physical contact. These findings reveal that children and adults consider the costs imposed on owners when judging the acceptability of people’s interactions with others’ property. Critically, these findings also provide preliminary evidence that children’s concept of ownership may be embedded into their broader social cognitive framework of intuitive psychology.

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)0.009
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 score0.009
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.002
Scholarly communication0.0030.003
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.024
GPT teacher head0.319
Teacher spread0.295 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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