The role of Euphemisms in Nzema Language and Culture
Bibliographic record
Abstract
This paper examines the role of euphemisms in the context of Nzema. It however discusses the Nzema examples alongside Akan, a sister language. Euphemisms can be linked to bone marrow in the sense that they are inseparable from language just as marrow is with bone. That is to say, it is highly impossible for bones not to have marrow as they are already embedded or built into the bones for varying purposes. Inasmuch as a body without breath is lifeless, a language that has no euphemistic expressions can also be said to be inadequate with respect to its functional and stylistic aspects. We use language to communicate and euphemism is a proper language style that people pursue in social communication in order to reach an ideal communication effect. Euphemisms are used in place of some sensitive, unpleasant, disturbing and taboo topics. The Nzema data indicate that in this language (Nzema), these expressions (euphemisms) can be dichotomised into two categories based on the communicative function they hold. In the first place, euphemisms are motivated by issues relating to taboos and are mainly used to avoid affronting both speaker’s and the hearer’s face. Secondly, they function as a stylistic marker so that their use is not as a response to taboo topics, rather, are subtle means of expressing one’s thought which fit in the context of use.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.018 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.004 |
| 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".