Bibliographic record
Abstract
Euphemism, a common linguistic phenomenon in different cultures, is defined on the lexical-level in this thesis. It refers to “the substitution of an agreeable or inoffensive expression for one that may offend people or suggest something unpleasant”. This thesis is to make a contrastive study of English euphemisms and Chinese euphemisms by means of the relevant linguistic theories, by which we conclude that euphemism is a linguistic, and particularly a cultural phenomenon. Its development is the outcome of various socio-psychological factors. The study of English euphemisms will surely shed light on English teaching in China. Key words: Euphemism; Culture; Intercultural communicationResumé: L'euphémisme, un phénomène linguistique commun dans des cultures différentes, est défini dans cette thèse au niveau lexical. Il se réfère à la "substitution d'une expression agréable ou inoffensive à celui qui peut offenser les gens ou suggérer quelque chose de désagréable". Cette thèse est de faire une étude contrastive sur l'euphémismes en anglais et en chinois en utilisant des théories linguistiques pertinentes, à partir desquelles nous concluons que l'euphémisme est un phénomène linguistique, et en particulier culturel. Son développement est le résultat de divers facteurs socio-psychologiques. L'étude des euphémismes anglais va sûrement faire la lumière sur l'enseignement de l'anglais en Chine.Mots-clés: euphémisme; culture; communication interculturelle
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".