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Record W2165316507 · doi:10.5539/jel.v1n2p72

Morphophonemic Analysis of Inflectional Morphemes in English and Ibibio Nouns: Implications for Linguistic Studies

2012· article· en· W2165316507 on OpenAlexvenueno aff
Ubong Ekerete Josiah, Juliet Charles Udoudom

Bibliographic record

VenueJournal of Education and Learning · 2012
Typearticle
Languageen
FieldPsychology
TopicPhonetics and Phonology Research
Canadian institutionsnot available
Fundersnot available
KeywordsMorphemeLinguisticsMorphophonologyPhonologyAgglutinative languageNounSyntaxSemantics (computer science)Computer sciencePhilosophy

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.085
Threshold uncertainty score0.158

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.072
GPT teacher head0.457
Teacher spread0.386 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations11
Published2012
Admission routes1
Has abstractyes

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