Nigerian Pidgin: The Identity of a Nigerian Away from Home
Bibliographic record
Abstract
In a rapidly globalizing world, language plays a vital role in shaping identity, especially within diasporic communities. This study explores how Nigerian-Canadian speakers engage with Nigerian Pidgin (NP) and how their linguistic behaviors reflect broader socio-cultural and educational trends. Despite being Nigeria’s most widely spoken language, NP has historically been marginalized and perceived as a ‘broken’ form of English (Agheyisi, 1971; Akande & Salami, 2010). While previous research has examined NP’s role in education (Akande, 2010) and its potential as a national language (Mann, 2009), its function in shaping diasporic identity remains underexplored. Using a mixed-methods approach, this study analyzes multilingual practices among 30 Nigerian-Canadians in the Greater Toronto Area (GTA) across three age groups (younger: 18–29; middle-aged: 30–49; older: 50+). Findings reveal generational and educational differences in attitudes toward NP. Younger speakers prioritize English for socioeconomic mobility and integration into Canadian society, while middle-aged and older speakers use NP more frequently as a cultural marker. Contrary to expectations based on covert prestige (Trudgill, 1972), female speakers expressed more positive attitudes toward NP than men. Matched-guise results indicate that a female speaker was rated more favorably in English, whereas the male guise received higher ratings in NP, reflecting gendered linguistic perceptions (Eckert & McConnell-Ginet, 2003). Education also influenced attitudes, with postgraduates showing stronger support for NP as an identity marker compared to undergraduates. Additionally, longer residence in Canada correlated with a preference for English over NP, suggesting a linguistic shift over time (Fishman, 1991). These findings highlight NP’s complex role in identity construction, solidarity, and integration among Nigerian-Canadians in the GTA. While NP fosters transnational ties, its perception remains context-dependent, shaped by stereotypes and sociolinguistic hierarchies, and this reflects hierarchies about pidgins and creoles more generally (DeGraff 2003, 2005). Overall, this study contributes to broader discussions on language, migration, and diasporic identity, emphasizing the evolving status of NP within and beyond Nigeria. Future research should further examine NP’s role in pan-West African identity and intergenerational language transmission in diasporic contexts.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 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".