Desenvolvimento profissional docente colaborativo em Educação Física
Bibliographic record
Abstract
Práticas colaborativas no desenvolvimento profissional docente em Educação Física (DPD-EF) têm sido cada vez mais apoiadas devido aos benefícios reconhecidos para professores e alunos. Ao apresentar esta seção Em Foco, este artigo visa ampliar e aprofundar as conversas entre pesquisadores sobre as práticas colaborativas no DPD-EF. Codesenhado como um projeto colaborativo de dois anos sobre práticas colaborativas, esta edição especial buscou colaboração e solidariedade, compartilhamento de conhecimento e negociação de desafios na internacionalização da pesquisa. Pesquisadores do Brasil, Austrália, Canadá, Irlanda, Portugal, Nova Zelândia, Estados Unidos e Turquia trabalharam em seis grupos para discutir quatro temas: (a) tipos de DPD-EF colaborativo, (b) facilitação de DPD-EF, (c) metodologias inovadoras e, (d) o desenvolvimento de experiências colaborativas. Ao final, esperamos destacar os desafios e contribuições que podem melhorar as pesquisas e experiências daqueles que formam, concebem e participam de práticas colaborativas de DPD-EF em todo o mundo.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".