Linguistic Manipulation of Political Myth in Margaret Atwood’s The Handmaid’s Tale
Bibliographic record
Abstract
This paper investigates the linguistic manipulation of political myth in Margaret Atwood’s The Handmaid’s Tale. More specifically, this paper discusses the myth of the good-of-the nation, which is linguistically manipulated verbally and nonverbally throughout the novel. Atwood’s novel is one of the distinguished dystopian narratives in the twentieth century. This type of fiction has always been a reflection of the irrationalities committed against people by those in power. Drawing on two approaches of political discourse analysis (Chilton, 2004; Wodak, 2009), this paper tries to answer one research question: How are political discourse strategies employed linguistically to propagate the good-of-the-nation myth? By making a connection between the data extracted from the selected novel and the way present regimes use language, this paper aims to explore the extent to which the good-of-the-nation myth is linguistically manipulated to dominate the public. As such, this paper attempts to provide the public with some sort of linguistic knowledge so as for them to be aware of the manipulative use of language in shaping and/or misshaping public attitudes. Lexical choices, didactic indoctrination, religionisation and dehumanisation are among the strategies used in the analysis of the selected data. There are two main findings in this paper. First, different linguistic levels of analysis are incorporated to propagate the discourse of political myth in the selected novel: the lexical, the pragmatic, the grammatical and the morphological. Second, political myths are linguistically manipulated to normalise their initiators’ erroneous practices and legitimise their irrationalities.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.011 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".