Bibliographic record
Abstract
Introduction The Rift Valley area of central and northern Tanzania is of considerable interest for the study of language contact, since it is unique in being the only area in Africa where members of all four language families are, and have been, in contact for a long time, having had linguistic interaction of various intensity at various points in time, which is reflected by convergence in parts of their grammatical structures (see map 6.1). The modern languages that took part in this linguistic contact are the West Rift languages of Southern Cushitic (Iraqw, Gorwaa, Alagwa, and Burunge), the Datooga dialects of Southern Nilotic, some Bantu languages of the F zone (Nyaturu, Rangi, Mbugwe, and maybe Nilyamba, Isanzu, and Kimbu), and Sandawe and Hadza, the Khoisan languages of eastern Africa. Actually, in the absence of any unambiguous indication that Hadza is genetically linked to Khoisan, it is better to be considered a linguistic isolate; see Sands (1998). The fact that the languages involved come from different, genetically unrelated families makes this area very promising for the study of language contact in that similarities between languages have five possible explanations: (i) universal properties, (ii) chance, (iii) borrowing or diffusion, (iv) retention, or (v) parallel development (Aikhenvald & Dixon 2001).
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".