A sociolinguistic analysis of plural marking in Nigerian Pidgin English
Bibliographic record
Abstract
This study examines plural marking in Nigerian Pidgin English (NPE) in light of language change. The plural marking strategies used in NPE are the morphological plural -s, postnominal dem, zero marking and reduplication of adjectives/nouns, double marking, numerals and quantifiers as analyzed in previous studies (Tagliamonte et al 1997, Ogunmodimu 2014). The idea for the language change analysis is that older studies show that dem or null marking are the most common plural marking strategies in NPE (Faraclas 1989, Mafeni 1971, Agheyisi 1971), while more recent studies (Tagliamonte et al. 1997, Ogunmodimu 2014) show that the plural marker -s is dominant in the language, while dem is disappearing. The current study arose from the question of the disappearance of dem as a plural marker in NPE as obtained from previous research. I believe an explanation for this disappearance could be that there is a change in the language as it relates to the use of plural markers. While previous studies have examined some linguistic and social factors that may influence the use of these markers (e.g. -s being sensitive to animacy and nominal reference (Tagliamonte et al. 1997) and dem to education (Ogunmodimu 2014), none of these works have looked at the variable- plural marking as a function of language change. In this thesis, I investigate age, gender and dominant language as factors that may influence the choice of plural markers used by NPE speakers in Winnipeg, Manitoba. I also investigate linguistic factors such as animacy, type of determiner and nominal reference, precipitated by a previous study by Tagliamonte et al. (1997). To better test for change in progress, the data of the study is collected through sociolinguistic interviews conducted in NPE, with 20 participants divided into two generations (older and younger) and gender (male and female) evenly distributed. All the speakers are immigrants in Winnipeg who have spent most of their lives in Nigeria to be able to acquire NPE. The results of the study suggest change in progress occurring in plural marking from the older use of zero marking to newer -s marking, as -s is still the dominant plural used in this study. Gender, dominant language and the interaction of age and gender are found to be significant factors in the choice of plural used by the speakers. The result that dominant language is significant supports the hypotheses of previous literature (Tagliamonte et al 1997 and Ogunmodimu 2014).
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| 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".