Bibliographic record
Abstract
Luganda, a language of Uganda, is like other Bantu languages in exhibiting a contrast between short and long vowels. In many positions, however, this contrast is neutralized, for example, before NC clusters, where it is reported that only long vowels appear. This paper investigates the extent to which such long vowels, which are also nasalized, are similar in duration to contrastively long vowels. Based on recordings of four native speakers of Luganda living in the US (two female), we compare the durations of vowels in the following contexts: (i) vowels before NT and ND clusters (N = nasal, T = voiceless stop, D = voiced stop); (ii) short vowels before T, D, N; and (iii) long vowels before T, D, N. Analysis is in progress and will also examine the acoustic properties of different vowels. Preliminary results show that the overall duration patterns of Luganda are consistent with those reported for several other Bantu languages. That is, vowels are indeed lengthened before NT/ND clusters; however, their average duration is somewhat shorter than that of contrastive long vowels (i.e., V: 149 ms; V before NC: 298 ms; V: 392 ms). Implications for the implementation of the vowel length contrast are discussed.
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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 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".