OF MIMICRY AND WOMAN: A FEMINIST POSTCOLONIAL READING OF WIDE SARGASSO SEA AND THE BIGGEST MODERN WOMAN OF THE WORLD
Bibliographic record
Abstract
Feminist and postcolonial studies have shown a similar concern with the production of new and more empowering subjectivities for those historically cast as subaltern in androcentric western contexts. In literature, as in criticism, concepts such as revision and subversion receive unprecedented attention as discourse, and hence narrative, begins to be seen as the very site where identity and relations of power are constructed and negotiated. Among the many women writers who sought to counterbalance the white maleness of the literary canon by giving colonized women a voice and a (hi)story are writers as diverse as the Dominican-born English writer Jean Rhys and the Canadian novelist Susan Swan. In their major fictional works, respectively Wide Sargasso Sea (1966) and The Biggest Modern Woman of the World (1983), they challenge the tradition of both literature and history by providing secondary or marginal women characters with a story of their own. Based primarily on the concepts of subversion and rearticulation proposed by Judith Butler and Homi Bhabha, this paper investigates and compares the strategies of representation employed by Rhys and Swan in the above novels with special attention to the relationship between the protagonists’ bodily experiences and the countries and cultures they stand for.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.016 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".