O ENSINO EXPLÍCITO: UM MEIO PARA TORNAR EFICAZ NOSSO SABER PEDAGÓGICO – ENTREVISTA COM CLERMONT GAUTHIER
Bibliographic record
Abstract
Clermont Gauthier e Professor Titular do Departamento de Estudos sobre o Ensino e a Aprendizagem na Faculdade de Ciencias da Educacao na Universidade Laval desde 1989. E titular da Cadeira de Pesquisa do Canada em estudos para a Formacao de Professores da Universidade Laval (Quebec) e membro fundador do Centro de Pesquisa Interuniversitaria para a Formacao e a Profissao Docente (CRIFPE). Ao longo de sua carreira universitaria publicou, como autor ou em colaboracao, mais de 40 livros e mais de uma centena de artigos e capitulos de livros sobre os temas pedagogia – suas origens e fundamentos – correntes pedagogicas, praticas pedagogicas eficazes e formacao de professores. A entrevista foi realizada em 18 de dezembro de 2012, ocasiao em que a entrevistadora realizava seu Doutorado Sanduiche na Universidade Laval de Quebec sob orientacao de Clermont Gauthier. As entrevistadoras acrescentaram notas explicativas, com o intuito de facilitar a compreensao de alguns aspectos.
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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 | 0.019 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.023 | 0.040 |
| Scholarly communication | 0.029 | 0.025 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.007 | 0.014 |
| Insufficient payload (model declined to judge) | 0.016 | 0.003 |
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".