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Formação inicial de professores de ciências na austrália, brasil e canadá: uma análise exploratória

2011· article· pt· W2124165169 on OpenAlexaff
Paulo Sérgio Garcia, Xavier Fazio, Debra Panizzon

Bibliographic record

VenueCiência & Educação (Bauru) · 2011
Typearticle
Languagept
FieldSocial Sciences
TopicEducation Pedagogy and Practices
Canadian institutionsBrock University
Fundersnot available
KeywordsHumanitiesPhysicsPhilosophy

Abstract

fetched live from OpenAlex

A principal justificativa para estudos comparativos em Educação é a promoção de um entendimento regional, local, por meio de análises e cooperação internacional. Na área do ensino de ciências, existem poucas investigações na formação de professores numa perspectiva internacional. Baseado nessa perspectiva, este artigo vem contribuir mostrando uma análise comparativa dos programas de formação de professores de ciências para o Ensino Fundamental em três diferentes contextos: Austrália, Brasil e Canadá. Apresenta-se uma análise qualitativa das similaridades e diferenças por meio da comparação da política de certificação de professores de ciências e das exigências das instituições formadoras numa específica jurisdição de cada país. Por meio dessa análise, identifica-se um número coerente de similaridades, destacando-se os mecanismos de funcionamento e as estruturas comuns que dão suporte aos programas de formação nas três realidades estudadas. Os resultados apresentados são importantes para futuros estudos comparativos na formação de professores de ciências.

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)0.010
metaresearch head score (Gemma)0.037
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 score0.224
Threshold uncertainty score0.451

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.037
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0050.013
Science and technology studies0.0060.003
Scholarly communication0.0050.002
Open science0.0010.004
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.000

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 head0.197
GPT teacher head0.406
Teacher spread0.209 · 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

Citations8
Published2011
Admission routes1
Has abstractyes

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