Borrowings But No Diffusion: A Case of Language Contact in the Lake Chad Basin
Bibliographic record
Abstract
Makary Kotoko, a Chadic language spoken in the flood plain directly south of Lake Chad in Cameroon, has an estimated 16,000 speakers. An analysis of a lexical database for the language shows that of the 3000 or so distinct lexical entries in the database, almost 1/3 (916 items) have been identified as borrowed from other languages in the region. The majority of the borrowings come from Kanuri, a Nilo-Saharan language of Nigeria, with an estimated number of speakers ranging from 1 to 4 million. In this article I first present the number of borrowings specifically from Kanuri relative to the total number of borrowed items in Makary Kotoko, and the lexical/grammatical categories in Makary Kotoko that have incorporated Kanuri borrowings. I follow this by presenting the linguistic evidence which not only suggests a possible time frame for when the borrowings from Kanuri came into Makary Kotoko, but also supports the idea that this is essentially a case of completed language contact. After discussing the lexical and grammatical borrowings from Kanuri into Makary Kotoko in detail, I explore the limited evidence in Makary Kotoko for lexical and grammatical ‘calquing’ from Kanuri, resulting in almost no structural diffusion from Kanuri into Makary Kotoko. I finish with a few proposals as to why this is the case in this instance of language contact in the Lake Chad basin.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.007 | 0.012 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".