ALTERNATIVA ŠEKSPIROVOJ „BURI”: GDE JE KALIBAN U ROMANU ĐAVOLJI NAKOT MARGARET ATVUD?
Bibliographic record
Abstract
The end of the 20th and the beginning of the 21st century saw the publication of many studies, interpretations, and adaptations of Shakespeare’s play “The Tempest.” One of the most significant recently published adaptations is the novel Hag-Seed: The Tempest Retold, by the Canadian author Margaret Atwood, created as a part of the Hogarth Press project and in celebration of the 400th birthday anniversary of William Shakespeare. The “Hag-Seed,” or the “devil’s seed,” an insult that Prospero directs to Caliban alluding to his origin, and at the same time the title of Atwood’s novel, unequivocally indicates to readers that the topic of this alternative Shakespeare’s “Tempest” might be Caliban. However, while most of the characters in Shakespeare’s play are easily recognizable in this adaptation set in the contemporary Canadian society, the character of Caliban is disembodied, fully reconstructed, and it indirectly reaches readers through the voices and characters of the Fletcher Correctional Center inmates. Analyzing Hag- Seed: The Tempest Retold from the perspective of postcolonial literary criticism, this paper concludes that Atwood’s exclusion of Caliban from the world of its adaptation cruelly depicts his dehumanization and the status of the “other” in Shakespeare’s play. The inmates easily identify with Caliban, his predicament, his subordinate position and attempt to oppose it. Demonized and marginalized by the society in which they live, they are also distant and unwanted “other” in the world of this modern “Tempest.” Relying in the analysis on a newer theory of adaptation according to which it is a creative process whose basic premise is to preserve the story from the original literary work, but also to create a new reading, and to suit the adaptation to an alternative purpose, function or environment, this paper examines the concept of the “other” and analyzes its transformation in the new environment.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.011 | 0.009 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".