VIAGENS NA MINHA TERRA E PETER PAN: A AUTOTEORIZAÇÃO LITERÁRIA E SUAS RELAÇÕES COM O ENSINO
Bibliographic record
Abstract
Partindo do pressuposto de que o principal conflito no ensino de Literatura no Ensino Médio é o grande enfoque na história da literatura, viu-se a necessidade de pensar o texto literário como protagonista. Entende-se que a abordagem exclusiva da história da literatura diminui a aparição do texto literário nas aulas, o que faz com que os alunos não tenham contato com o objeto de estudo da disciplina de Literatura, a obra literária. Com o intuito de colocar teoria e literatura lado a lado, optou-se por articular o mecanismo de autoteorização. Esse mecanismo visa abordar a teoria a partir das próprias obras literárias. Para explorá-lo foram selecionadas as obras Viagens na minha terra, de Almeida Garrett, e Peter Pan, de James Barrie. Como resultado das reflexões, será discutido de que forma a autoteorização literária contribui com o ensino de literatura no Ensino Médio. Serão utilizados: Culler; Volobuef; Jouve; e as OCEM.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.013 |
| Scholarly communication | 0.009 | 0.012 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".