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Record W6989842478

Children and Adults Consider Others' Resources When Inferring Their Emotions

2024· article· en· W6989842478 on OpenAlexfundno aff

Bibliographic record

VenueeScholarship (California Digital Library) · 2024
Typearticle
Languageen
FieldPsychology
TopicChild and Animal Learning Development
Canadian institutionsnot available
FundersSocial Sciences and Humanities Research Council of CanadaNatural Sciences and Engineering Research Council of Canada
KeywordsResource (disambiguation)ScarcityEvent (particle physics)Young adultResource scarcity
DOInot available

Abstract

fetched live from OpenAlex

The amount of resources someone has can influence their emotional responses to events. Two preregistered experiments investigated whether adults and children consider others' resource quantities when inferring their emotions. Sixty adults (Experiment 1) and 135 8-10-year-olds (Experiment 2) saw stories about people wanting an item but differing in the number of items they have enough money to buy (ranging from 1 to 5). Participants rated how these people felt both when buying the item and when losing it. Both adults and children judged that the fewer resources someone has, the sadder they felt when the item was lost, and the bigger emotional change they experienced (relative to when buying the item). Adults also judged that the impact of resource scarcity on emotion was most significant when the person had depleted all their resources, as opposed to still retaining some to influence the negative outcome, and this pattern is emerging in children. These findings suggest that even when the same negative event occurs, adults and children as young as 8 consider others' available resources when inferring their emotional responses to the event.

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)0.004
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 score0.003
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0010.001
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 head0.012
GPT teacher head0.226
Teacher spread0.214 · 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
Published2024
Admission routes1
Has abstractyes

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