A Pragmatic Analysis of Selected Nicknames Used by Yoruba Brides for In-Laws
Bibliographic record
Abstract
Names are generally used for identification in all human society. It has been observed by scholars working in this field that names perform more functions than ordinary means of identification. Following Austin (1967) speech act theory, it is observed that names perform some illocutionary acts which can help us maintain a peaceful cohabitation in our society. This work examined the use of nicknames by Yoruba brides for their in-laws. We analyzed those nicknames using pragmatic theories. The data was gathered within Ibadan and Akure metropolis and their remote settlements. Oral interview was used to compliment the intuitive knowledge of the researchers. Twenty-one (21) nicknames were selected for this study. Our findings revealed that, these nicknames are used for eulogizing, respecting and insulting/chastising. We therefore concluded that these nicknames be encouraged especially in the face of modernization that is eroding our culture and tradition of respect and appreciation.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 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) | 0.004 | 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".