Bibliographic record
Abstract
Abstract Several languages reported in the literature have at most three hiatus resolution strategies, for example: Igede (Bergman, 1971); Obolo (Faraclas, 1982); and Greek (Haas, 1988). Karanga, a dialect of Shona, has five strategies – coalescence, spreading (epenthesis), glide formation, secondary articulation and elision. The five form a conspiracy; they ensure that hiatus (VV sequences) never surfaces. The challenge is to determine which strategy is going to apply, including the formalisation of the analysis. I assume that in each domain, there is a preferred strategy: coalescence in the Cliticisation Domain; spreading in the Verbal Domain; glide formation, secondary articulation and elision in the Nominal Domain. Glide formation, secondary articulation and elision occur in phonologically conditioned complementary distribution. The approach employed in this article may be extended to other morphologically rich languages, where hiatus resolution strategies can be used as a diagnostic for determining different prosodic boundaries.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".