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Resolução colaborativa de problemas: resultados brasileiros no Pisa

2022· article· pt· W3217640086 on OpenAlexaff
Andriele Ferreira Muri Leite, Patrícia Ferreira Miranda, Nathalie Loye

Bibliographic record

VenueEnsaio Avaliação e Políticas Públicas em Educação · 2022
Typearticle
Languagept
FieldDecision Sciences
TopicBusiness and Management Studies
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsHumanitiesPsychologyPhilosophy

Abstract

fetched live from OpenAlex

Resumo A resolução colaborativa de problemas (RCP) é uma competência de extrema relevância na sociedade moderna e uma das mais valorizadas neste século. O Programa Internacional de Avaliação de Alunos (Pisa) foi a primeira avaliação em larga escala a incluir a resolução de problemas em sua matriz de referência. Esse estudo preliminar se restringe aos resultados publicados nos relatórios oficiais do Programa. Aqui, especificamente, discutimos esses resultados à luz da literatura de referência, bem como descrevemos o conceito de RCP no Pisa 2015. O Brasil, dentre os 52 países participantes, ocupou a 51ª posição nas habilidades relacionadas à RCP, ficando no Nível 1 da escala de desempenho. O status socioeconômico, a variação nas escolas e o gênero relacionam-se, positivamente, com o desempenho brasileiro em RCP.

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)0.008
metaresearch head score (Gemma)0.008
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Scholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.461
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.008
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0020.008
Science and technology studies0.0050.001
Scholarly communication0.0020.001
Open science0.0040.005
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0230.006

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 head0.088
GPT teacher head0.380
Teacher spread0.292 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
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

Citations1
Published2022
Admission routes1
Has abstractyes

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