Euphemism and Hegemony: Discursive Power of Communication across Cultures
Bibliographic record
Abstract
The socio-political manipulation of euphemisms across cultures as alternate metaphors with ideological force has been analyzed in the present paper. The study was inspired by George Orwell's treatment of euphemisms as ideological tools for hedging, Lakoff and Johnson's idea of metaphors as elements structuring human thought and Roman Jakobson's model to study metaphor and metonymy as instances of romantic and realistic tendencies respectively in the user, and ordering of human behavior accordingly. A close analysis of the employment of euphemisms in differing social set-ups suggests that some euphemisms reveal a hegemonic impulse behind their usage, while a different category of euphemisms behave as counter-balancing force against this hegemonic impulse exerting dominance in a community. To comprehend the significance of this distinction better, the researchers suggest that in the existing categorization of euphemisms, two new categories – hegemonic euphemisms and resistance euphemisms – may be added. Further investigation into the cultural function of euphemisms reveals that euphemisms function as signs of signs, therefore, meaningless words. The study concludes that such a usage of euphemisms is problematic since euphemistic expressions are capable of reducing (unwanted or undesirable) meaning as redundant, superfluous, and ineffectual to rouse human conscience. Keywords: euphemisms; cultural hegemony; cultural capital; symbolic power
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.035 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".