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Record W2519861256 · doi:10.5539/jedp.v6n2p113

Young Brazilian Children’s Emotion Understanding: A Comparison within and across Cultures

2016· article· en· W2519861256 on OpenAlexvenueno aff
Silja Berg Kårstad, Arne Vikan, Turid Suzanne Berg‐Nielsen, Pollyana de Lucena Moreira, Eloá Losano de Abreu, Júlio Rique

Bibliographic record

VenueJournal of Educational and Developmental Psychology · 2016
Typearticle
Languageen
FieldPsychology
TopicChild and Animal Learning Development
Canadian institutionsnot available
FundersHelse- og OmsorgsdepartementetNorges Teknisk-Naturvitenskapelige Universitet
KeywordsSocioeconomic statusPsychologyDevelopmental psychologyVariation (astronomy)ComprehensionSample (material)DemographySociologyLinguisticsPopulation

Abstract

fetched live from OpenAlex

Research on children’s Emotion Understanding (EU) has been dominated by middle-class samples from Western societies. We studied cultural and Socioeconomic Status (SES) variation in young children’s EU in a high SES sample (n = 50) and a low SES sample (n = 50) of Brazilian preschoolers using the Test of Emotion Comprehension. We found that the high SES sample performed better at both the overall and component levels than the low SES sample on EU. The differences were especially substantial for the recognition of basic emotions, with the low SES children recognizing negative emotions better than positive and neutral emotions. In addition, we compared the two SES samples of Brazilian children to same-age samples from Norway, Italy and Peru. Between the Brazilian and the European samples and the Brazilian and other non-European samples, the variation in EU was observed to be more related to SES than to culture.

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 score0.049
Threshold uncertainty score0.097

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.043
GPT teacher head0.377
Teacher spread0.334 · 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

Citations5
Published2016
Admission routes1
Has abstractyes

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