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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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 teacher head, 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".