Singular-Plural Distinction in Izon and its Influence on the Teaching/Learning of Plural Formation in English
Bibliographic record
Abstract
This study examines singular-plural distinction processes in Izon and highlights the difficulties these may pose tothe teaching and learning of plural formation in English so as to suggest ways in which teachers can design aneffective teaching method to tackle the perceived difficulties. The study, which made use of 100 subjects of anaverage age of eleven years drawn from the Arogbo-Izon community of Ondo State, Nigeria, reveals that Izoninhibits the learning of plural formation in English as the majority of the subjects exhibit the influence of the apluralitymarker and the reflexive pronoun formation process in Izon thereby pluralizing all English nounsthrough the addition of -s and deriving the reflexive pronoun (their selfs or their selves) through the addition ofself to the possessive form of the pronoun (their) as against the object form (them) preferred in English. Toensure that the subjects are assisted to overcome these difficulties, the study employs a ten-step contrastiveapproach which proves very effective as the subjects’ performances, after the application of the method, recordeda tremendous rise in the percentage of correct responses from 26 per cent to 94 per cent (plural formation innouns) and from 46 per cent to 100 per cent (plural formation in pronouns). The method is, therefore,recommended for the teaching of English in the Izon communities in Nigeria and in other similar ESL situationsboth within and outside Nigeria.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".