Morphophonemic Analysis of Inflectional Morphemes in English and Ibibio Nouns: Implications for Linguistic Studies
Bibliographic record
Abstract
Linguists generally acknowledge that there exists an inevitable inter-relationship between different levels oflinguistic analysis---phonetics, phonology, morphology, syntax and semantics. Various linguistic labels are usedto describe such a link. In particular, there exists a bridge between the phonology and morphology of particularlanguages. The term “morphophonemics” is generally used to describe linguistic statements that can be made ofthe phonemic structure of morphemes and their effect on the grammatical content of languages. This paperbasically attempts a morphophonemic analysis of inflectional morphemes of nouns in two structurally andhistorically distinct languages (English and Ibibio) in order to discover points of differences and similaritiesusing the Contrastive Analysis (CA) model of investigation as its theoretical framework. The results indicate thatthe two languages are structurally different. For instance, Ibibio is agglutinative, tonal and analytic in naturewhile English is basically analytic and intonational. The paper, therefore, analyzes the problem that the Ibibiospeaker of English is likely to encounter in the study of the English word structure. Again, based on its findings,the paper corroborates Greenberg’s (1964) and Essien’s (2003) classifications of African and Southern Nigerianlanguages respectively.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".