Intertekstinis Margaretos Atwood romano Tarnaitės pasakojimas skaitymas | An Intertextual Reading of Margaret Atwood’s The Handmaid’s Tale
Bibliographic record
Abstract
Since its publication in 1985, Margaret Atwood’s dystopia The Handmaid’s Tale has prominently secured its place among the most widely read novels of Canadian literature. The present article offers a reading of Atwood’s The Handmaid’s Tale as a narrative built on intertextual relationships with other texts, which are directly or implicitly referred to in the novel. This analysis of the novel emphasizes reading as a dynamic semiotic process and employs Gérard Genette’s notion of transtextuality to look into the narrative’s semantic field from the perspective of its paratextual, intertextual and metatextual relationships. While focusing on the Bible as the major intertext that structures the semantic space of Atwood’s narrative, this intertextual reading of The Handmaid’s Tale also looks at the ways in which the biblical context of Atwood’s novel correlates with the paratexts of Jonathan Swift’s essay A Modest Proposal and sufi philosophy to delineate the dominant strategies of the reading and interpreting of Atwood’s text. Finally, the article analyzes the metatextual level of Atwood’s novel and points out the ways in which the writer ironizes the intertextual nature of her own text and fictionalizes its intertexts, thereby suggesting the impossibility of narrative and interpretative closure.
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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 | 0.008 | 0.010 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".