Testing the Intelligibility of Nigerian Fali to Cameroonian Fali, Bana and Gude Listeners
Bibliographic record
Abstract
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.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".