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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

<p>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.</p>

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.046
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 teacher head, not a consensus.

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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