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Record W2913836132 · doi:10.5539/ells.v9n1p24

Investigating the Phonological Processes Involved When Yoruba Personal Names Are Anglicized

2019· article· en· W2913836132 on OpenAlexvenueno aff
Eunice Fajobi, Bolatito Akomolafe

Bibliographic record

VenueEnglish Language and Literature Studies · 2019
Typearticle
Languageen
FieldArts and Humanities
TopicLinguistics, Language Diversity, and Identity
Canadian institutionsnot available
Fundersnot available
KeywordsYorubaLinguisticsPhonologyContext (archaeology)HistoryPsychologyPhilosophy

Abstract

fetched live from OpenAlex

Personal names, in African context, are not arbitrary. They are like signposts that convey a wide range of invaluable information about the bearers. Also, they are like a ‘social DNA’ that discloses the identity, family background, family history, family vocation and family deity of the bearer (Onadipe, 2012). Sadly however, studies, which are mostly sociolinguistic in perspective, abound to show that some of these given personal names are being anglicized among the younger generation of bearers (Soneye, 2008; Faleye & Adegoju, 2012; Raheem, 2013; Filani & Melefa, 2014). From the standpoint of socio-phonology and using Knobelauch’s (2008) Phonological Awareness as our theoretical framework, this paper investigates the phonological changes that Yoruba personal names undergo when they are anglicized; and their implication for the endangerment of Yoruba language. Perceptual and acoustic analyses of the data sourced from the written and verbalized (as well as recorded) anglicized names of 50 informants from a Nigerian University show “stress-shift” as the major prosodic strategy used by speakers to anglicize Yoruba personal names. Other phonological processes identified include re-syllabification, contraction, elision and substitution; but bearers are not overtly aware of these processes. Findings reveal further that though the “new names” are structurally more English than Yoruba, they are nevertheless pronounced with Yoruba tone by some bearers.

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)0.000
metaresearch head score (Gemma)0.010
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.053
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.000
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 head0.022
GPT teacher head0.233
Teacher spread0.210 · 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.

Study designQualitative
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

Citations4
Published2019
Admission routes1
Has abstractyes

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