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Record W4394999187 · doi:10.53103/cjlls.v4i2.160

Testing the Intelligibility of Nigerian Fali to Cameroonian Fali, Bana and Gude Listeners

2024· article· en· W4394999187 on OpenAlexvenueno aff
Abbo Garou, Michael Etuge Apuge

Bibliographic record

VenueCanadian Journal of Language and Literature Studies · 2024
Typearticle
Languageen
FieldComputer Science
TopicLinguistic Studies and Language Acquisition
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineAudiology

Abstract

fetched live from OpenAlex

Using the word 'Fali' to identify a group of people and languages in northern Cameroon and Nigeria has caused significant challenges to the classification of the languages and the description of the link between those communities. This study aims at testing the intelligibility of Nigerian Fali to Cameroonian Fali (CamFali), Bana and Gude speakers in order to bring out the relationship between the languages. Participants to the study include 88 Cameroonian native and fluent speakers of the above listed languages, made up of 46 CamFali speakers from the Bossum, Kangu, Peske-Bori and Tinguelin communities, 35 Gude speakers from Gude dialect (Gude D), Djimi and Njanyi, and 7 Bana speakers. The informants were met either in-person or online (through WhatsApp), and they listened to 3 recordings from 3 dialects of Nigerian Fali, namely Vimtim, Bahuli and Muchalla which were obtained from Global Recordings Network to test the listeners' recognition and comprehension of the languages. The findings indicated that the 3 languages are neither intelligible to CamFali nor to Bana listeners, but they are rather variants of Djimi which in turn is a dialect of Gude. The study also revealed that from the 3 dialects of Gude, Djimi is more related to Gude D than Njanyi. It was therefore concluded that Nigerian Fali is not related to CamFali and Bana, but it is rather Gude, which confirms that the word 'Fali' does not refer to specific people with common ancestral or linguistic background.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.418
Threshold uncertainty score0.304

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.020
GPT teacher head0.284
Teacher spread0.264 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations0
Published2024
Admission routes1
Has abstractyes

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