A tempestade: de Shakespeare a Atwood
Bibliographic record
Abstract
A remarkable feature of William Shakespeare’s creative genius is a taste for adaptation. While he transposed stories from his and other cultures to the creation of his plays and poems, several other authors have adapted and continue to adapt the stories ofthe English playwright. An example of this phenomenon is the Canadian writer Margaret Atwood, who transposed the dramaturgy The tempestinto the novel Hag-seed, in the Brazilian translation, Semente de bruxa. From the perspective of this process, we understand the initial work as a reference for the final work that, however, acquires new characteristics and specificities of the language with which it works, while maintaining a dialogue with the initial work. It is understood in the articulation between both artistic languages in question, dramatic and narrative, beyond the mere construction of the fable, the resources that are used in this new artistic process. Thus, the scope of this research is to recognize the process of adaptation from the play into thenovel and to reflect on its procedures. Therefore, this thesis is based mainly on the theory of adaptation by Julie Sanders (2006) and Linda Hutcheon (2013), as well as on the theories of the Shakespearean theater (BRADBROOK, 1968; FRYE, 1992; HELIODORA, 1997; KOTT, 2003) and the novel (CANDIDO et al, 2005; TODOROV, 2006).
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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 | 0.006 | 0.005 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".