Social and Cognitive Implications of Using Euphemisms in English
Bibliographic record
Abstract
Today in a globalized society the need for word substitutions while speaking on sensitive topics has increased. People search for milder alternatives to express their opinions whenever they feel their words might sound abrupt or offensive. These substitutions are called euphemisms. At first sight one might suppose that these expressions are too ordinary, but in fact they possess a strong persuasive character. Thus, the subject of this article is to identify the main functions of euphemisms in modern society. The article also aims at determining which social and cognitive factors regulate our choice of these substitutions. In the theoretical part of the research different views on the phenomenon are outlined. The main method used in this work is descriptive analytical method, based on the description of euphemisms from theoretical point of view with the subsequent analysis of achieved results. Besides, the method of contextual analysis has been applied. As data for analysis different euphemistic expressions have been studied. An overall study shows that in modern life honest debate has turned into a rare phenomenon. One of our assertions is that the use of euphemisms primarily presupposing good intentions so as not to hurt a listener’s feelings, in modern life has acquired completely a different purpose. Today people use euphemisms to sound more persuasive instead of simply sounding polite. It should be noted that for a deeper understanding of the role of euphemisms they should be studied within a specific discourse. Thus this study will require a further look at the problem applying a more contextual approach to its analysis.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.012 |
| Scholarly communication | 0.005 | 0.004 |
| 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".