Peri em Brocéliande: o deserto-floresta n’O Guaraní, de José de Alencar
Bibliographic record
Abstract
This article seeks to articulate the representation of the landscape and its connection with the character of the hero in O guarani, by José de Alencar, the masterpiece of Brazilian Romanticism; in other words, it sseks to articulate space and normativity as essential components of narrative.Since Gaston Bachelard, space has been configured as an important instance for fiction, but only from the analysis of Iuri Lotman and Henri Mitterand it will cease to be accessory, in order to become decisive for the organization of the plot. Lotman coined the notion of frontier, while Mitterand proposed the concept of overcoming frontiers. Both definitions are fundamental to understand the process of constitution of the hero in Alencar´s novel, since Peri will be the only character to move freely through the spaces of the “house” and the “forest”, incorporating values of one and the other. Dialoging with the medieval narratives of Chrétien de Troyes, particularly “Le chevalier au lion” and “Le conte du Graal”, the hero of O guarani breaks, however, with the chivalrous code of honour by adopting treason as a regular procedure. It will be the forest, as the central topos of the medieval imagery (Le Goff), which will provide such a hybrid and ambivalent stature of the hero.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
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".