Bibliographic record
Abstract
While previous studies on Dàgáárè tone have looked at the nouns, this paper particularly examines tone in verbs, perfective vs imperfective forms. The verbal system has different patterns based on the form of the verb. There are three tone classes for Dàgáárè verbs and for each of the classes, the surface tone pattern it exhibits in the perfective is systematically different from the tone patterns in the imperfective. For the perfectives we have L, H and HL while the imperfectives have LH, HL and H!H, at least in the dialect under study. I treat tone as a combination of the features [±upper] and [±raised] which are connected to what is described as a Tone node (T-node). These Tone nodes in turn connect to the syllable. Under this system, I assume L is represented with the features [-upper] and [-raised] and H with the features [+upper] [+raised]. Underlying tonal melodies of the root morphemes are identical to the surface tones of the perfective forms whether these contain an overt suffix or not. For the imperfectives, the suffix comes with an unspecified underlying T-node. The grammar then chooses the features [±upper] and [±raised] to insert under the already existing T-node.
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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 | 0.001 | 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) | 0.002 | 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".