Nigerian students’ attitudes toward endonormative varieties of Nigerian English
Bibliographic record
Abstract
Previous language attitude research in Nigeria compared Nigerian English (NgE) to exonormative Englishes, such as British, American and Canadian Englishes, whereas this study examines attitudes toward endonormative varieties (Hausa English, HE; Igbo English, IE; and Yoruba English, YE) of NgE. Four hundred and six students drawn from three Nigerian universities located in the regions where these varieties are mainly spoken served as listener judges in a verbal-guise experiment. The results indicate that YE received higher ratings on status, solidarity and quality of language dimensions than HE or IE. Whilst YE received the highest ratings, HE was consistently rated as the least attractive. The study shows that the higher a variety was rated on the quality of language dimension, the higher it was rated on status and solidarity. The results indicate that, unlike in many linguistic contexts where more favourable linguistic demographic profiles accompany varieties with greater political status, this is not the case in Nigeria’s sociolinguistic context. These findings are discussed in light of ethnolinguistic vitality and the history and social development of Nigeria. This study allows for a better understanding of stereotype formation in educational settings and language-based stigma toward varieties of NgE.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".