Linguistic Representation of Power in Edward Bond’s Lear: A Lexico-Pragmatic Approach to Critical Discourse Analysis
Bibliographic record
Abstract
This paper investigates the linguistic representation of power in Edward Bond’s Lear (1978). More specifically, the paper tries to explore the extent to which power is linguistically represented manipulatively and/or persuasively by means of specific lexical and pragmatic devices in the discourse of the selected play. The main objective of the paper is to explore how power relations, irrespective of their type, influence the cognitive world of the discourse participants, which in turn attempts a change in their conversational behavior to the extent that allows the acceptance of a specific argument in a particular way. The paper adopts a lexico-pragmatic perspective to Critical Discourse Analysis (CDA), instanced by Fairclough’s (1989) lexical model for the analysis of discourse, and the concepts of politeness and face (Thomas, 1995; Yule, 1996a). The main research question of the paper is: to what extent do different power relations, encoded lexically and/or pragmatically, affect the conversational behavior of the play’s characters, persuasively and/or manipulatively? Some lexical and pragmatic strategies have been highlighted and then linguistically analyzed to expose their effectiveness in deciphering persuasive and manipulative power relations in the selected play. Among these strategies are: euphemism, myth-making, positive self-presentation and negative other-presentation, and politeness strategies. The paper concludes that power has linguistically been encoded in the discourse of the selected play, both persuasively and manipulatively, to affect a cognitive shift in behavior reflected in the conversational interaction among characters.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.018 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.003 |
| 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".