On the origin of some Northern Songhay mixed languages
Bibliographic record
Abstract
This paper discusses the origins of linguistic elements in three Northern Songhay languages of Niger and Mali: Tadaksahak, Tagdal and Tasawaq. Northern Songhay languages combine elements from Berber languages, principally Tuareg forms, and from Songhay; the latter provides inflectional morphology and much of the basic vocabulary, while the former is the source of most of the rest of the vocabulary, especially less basic elements. Subsets of features of Northern Songhay languages are compared with those of several stable mixed languages and mixed-lexicon creoles, and in accounting for the origin of these languages the kind of language mixing found in Northern Songhay languages is compared with that found in the (Algonquian) Montagnais dialect of Betsiamites, Quebec. The study shows that Tagdal and the other Northern Songhay languages could be construed as mixed languages, although the proportion of Berber and Songhay elements varieties somewhat between these languages, and also indicates that the definition of ‘mixed language’ is labile because different mixed languages combine their components in different ways, so that different kinds of mixed languages need to be recognized. NS languages seem to belong to the category of Core-Periphery languages with respect to the origins of more versus less basic morphemes.
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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 | 0.000 | 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 | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| 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".