Bibliographic record
Abstract
Two topics in the front burner of contact linguistics are bilingualism and code switching. Code switching between an indigenous language and the English language is pervasive where outer circle Englishes are spoken. Nigeria and other former colonies of Great Britain belong to this circle of Englishes. This study discusses nouns functioning as lexical heads in Urhobo/ English code switches. The switches include code switched NP[Z1] with determiner[Z2] s from Urhobo and head word[Z3] s from English; Urhobo –English complex Code switched NP with an adjective; complex CS NP with an adjective[Z4] prepositional phrase as complement. The study is premised on two theories. First is Noam Chomsky’s (1981) principles and parameters theory of transformational grammar which has been used for the analysis of the sentences. The second is Myers-Scotton’s (2002) Matrix Language Frame (MLF), which is used for distinguishing between the matrix and the embedded languages in the nominal phrasal constituents within the code switched sentences. In this study, Urhobo is the matrix language while English is the embedded[Z5] language based on matrix language frame (MLF) parameters. The study concludes that nouns functioning as lexical heads constitute part of the structural basis of Urhobo English code-switching. [Z1]NPs [Z2]determiners [Z3]words [Z4]adjective [Z5]embedded language
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| 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".