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Record W3162538296 · doi:10.1111/cdev.13581

Toddlers and Preschoolers Understand That Some Preferences Are More Subjective Than Others

2021· article· en· W3162538296 on OpenAlexafffundabout
Tiffany Doan, Ori Friedman, Stephanie Denison

Bibliographic record

VenueChild Development · 2021
Typearticle
Languageen
FieldPsychology
TopicAnimal and Plant Science Education
Canadian institutionsUniversity of Waterloo
FundersSocial Sciences and Humanities Research Council of CanadaNatural Sciences and Engineering Research Council of Canada
KeywordsPsychologyDevelopmental psychologyPreferenceSocial psychologyAppeal

Abstract

fetched live from OpenAlex

Four experiments examined Canadian 2- to 3-year-old children's (N = 224; 104 girls, 120 boys) thoughts about shared preferences. Children saw sets of items, and identified theirs and another person's preferences. Children expected that food preferences would be more likely to be shared than color preferences, regardless of whether the items were similar or different in appeal (Experiments 1-3). A final study replicated these findings while also exploring children's expectations about activity and animal preferences. Across all studies, children expected shared preferences at surprisingly low rates (never higher than chance). Overall, these findings suggest that young children understand that some preferences are more subjective than others, and that these expectations are driven by beliefs about domains of preferences.

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)0.001
metaresearch head score (Gemma)0.005
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 score0.146
Threshold uncertainty score0.291

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.002
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.057
GPT teacher head0.287
Teacher spread0.230 · 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

Citations6
Published2021
Admission routes3
Has abstractyes

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