Morphological Features in the English Language of Selected Nigerian Paramilitary Formations in Akwa Ibom State
Bibliographic record
Abstract
This paper seeks to investigate the official communicative activities among Nigerian paramilitary formations in Akwa Ibom State, with a view to determining the peculiar forms generated through morphological processes that occur in their interactions. The agencies selected for the study are: the Nigeria Police Force, the Federal Road Safety Corps and the Nigeria Security and Civil Defence Corps. The theoretical framework adopted for, and considered relevant to the study is Lieb’s Theory of Process Model of Word Formation. The theory offers a comprehensive approach which accounts for all processes of word formation in a unified way. Data used for the study were collected through participant observation and unstructured interview of personnel while on duty within the office environment using random sampling method. The findings indicate that the operatives used English language for their formal conversations to communicate paramilitary ideology. In addition, they were found to have used unique lexical choices created specifically to serve the communication needs of the interlocutors. It is therefore recommended that operatives of these agencies should ‘simplify’ their morphologically-conditioned terminologies in particular, and the language in general, such that the public which they are meant to serve could understand.
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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 | 0.001 | 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 | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".