Determiners and Adjectives in English and Nigerian Pidgin - A Contrastive Inquiry
Bibliographic record
Abstract
This paper undertakes a contrastive analysis of determiners and adjectives in English and Nigerian Pidgin. It observes that there are sub-divisions of determiners; which are pre-determiners, determiners and post-determiners and it discusses the form class of determiners in English and Nigerian Pidgin. This paper notes that Nigerian Pidgin has fewer determiners than English. The sub-classes of adjectives in the two languages are analyzed. It evaluates the difference between English and Nigerian Pidgin adjectives. The intensifiers that precede adjectives and the comparative and superlative degrees of comparison in the two languages are discussed. Finally, the paper notes that the observed differences between determiners and adjectives in the two languages creates learning problems of split and collapsing for the Nigerian Pidgin speaker learning English determiners and adjectives.
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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 | 0.001 | 0.021 |
| 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 | 0.002 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".