Rewriting Shakespeare: Translation and Analysis of Margaret Atwood's Hag-Seed
Bibliographic record
Abstract
"Hag-Seed” is not only the name by which Prospero defines Caliban’s mother, Sycorax, in Shakespeare’s The Tempest, but it is also the term used by the Canadian author Margaret Atwood to title her book. Her novel, Hag-Seed, will be the translating subject of this dissertation. The translation and its commentary will be preceded by an in-depth study of The Tempest, through some key readings of Shakespeare's work, ranging from colonial to feminist interpretation. The ultimate aim is to discover and analyse how such readings were transferred and translated into Margaret Atwood’s novel. The main questions are: what are the peculiarities of Shakespeare that make him contemporary and immortal? How can a major seventeenth-century classic be adapted for a twenty-first-century audience? Precisely, this dissertation is divided into four chapters. The first chapter focus on the concept of “Hypertextuality” and the notion of “Adaptation Theory”, studied through works such as "Palimpsests: Literature in the Second Degree" by Gérard Genette, and "The Adaptation Theory" by Linda Hutcheon. The second chapter deals with one of the most accredited readings of The Tempest: the post-colonial interpretation. Central to this chapter is the concept of prison, meant both as physical space but also as the constriction of bodies and lack of freedom. In particular, I will refer to a specific type of prison, called Panopticon. In the third chapter we move on to a feminist interpretation of The Tempest, which could not be missed considering the importance that such a reading has in Magaret Atwood's works and life. The fourth and final chapter focuses on the translation commentary. This will be followed by the last part of this thesis, the translation of the chapters I considered most relevant from Margaret Atwood's novel.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.009 | 0.009 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".