Bibliographic record
Abstract
Résumé We have two aims here. One is to provide an inventory and typological overview of the commonest pre-stem and suffixal tense-aspect markers across Bantu. We examine geographical distribution, phonological and tonal shape, and general semantic range. The other is to ask which of these might be assigned to Proto-Bantu, some 5000 years ago. We use a database of 100 languages, comprising 85 from all Guthrie's groups (A10, A20, etc) plus another 15 from his 15 zones. The most widespread pre-stem markers are: /a/, which comes in several tonal and vowel-length variations, representing ‘past’ in most languages and ‘non-past’ (possibly older focus (Nurse 2006)) in fewer languages; zero ‘general present’; /ka/ ‘itive, narrative, (far) past, (far) future’; /ki/ ‘persistive, participial’; /laa/ ‘future’ and /la/ ‘focus’. The first three certainly go back to Proto-Bantu, the status of the last three is less certain. The commonest suffixes are: /a/ ‘neutral’; /e/ ‘subjunctive’; /ile/ ‘perfect, past’; /ag/ ‘imperfective’; /i/ ‘positive near past’; a vowel copy suffix ‘positive near past’. The first five go back to Proto-Bantu, the sixth is innovation. We propose that /ile, i, the vowel copy suffix/ are connected. Finally, we mention four widespread but derived pre-stem markers.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".