Bibliographic record
Abstract
Although verb forms encoding focus were recorded in various Bantu languages during the twentieth century it was not until the late 1970's that they became the centre of serious attention, starting with the work of Hyman and Watters. In the last decade this attention has grown. While focus can be expressed variously, this paper concentrates largely on its morphological, partly on its tonal expression. On the basis of morphological and tonal behaviour, it identifies four blocks of languages, representing less than a third of all Bantu languages: those with metatony, those with a binary constituent contrast between verb ("disjunctive") and post-verbal ("conjunctive") focus, those with a three-way contrast, and those with verb initial /ni-/. Following Güldemann's lead, it is shown there is a fairly widespread grammaticalisation path whereby focus markers may come to encode progressive aspect, then present tense. Many Bantu languages today have a pre-stem morpheme /a/ 'non-past' and it is hypothesized that many of these /a/, which are otherwise hard to explain historically, may derive from an older focus marker.
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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 | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".