Lexico-Semantic ‘Intraference’ in Educated Nigerian English (ENE)
Bibliographic record
Abstract
This paper examines the concept and features of lexico-semantic ‘intraference’ in Educated Nigerian English (ENE). The tendency of nonnative speakers in a(n) ESL setting, like Nigeria, to redeploy the lexico-semantic rules of English to ‘kill,’ weaken, strengthen and reverse the SBE and native English meanings of words is termed lexico-semantic intraference in this paper. Questionnaires, interviews, library research, empirical studies, the Internet and recording of live linguistic events were used to gather data from 2004 to 2013. It was discovered that educated Nigerians regularly impose meanings on some words, extend the meanings of words, weaken or reverse word meanings and also redeploy the lexico-semantic dynamics of the language to fabricate lexical items with new meanings or meanings already in some well established SBE words. These habits generate words and meanings that distinguish ENE lexico-semantics from the lexico-semantics of SBE and some other international varieties of English.
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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 | 0.001 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 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".