Tonal Polarity as Phonologically Conditioned Allomorphy in Munduruku
Bibliographic record
Abstract
This paper examines tonal polarity in Munduruku, a Tupi language spoken in Brazil. Munduruku contains a set of nouns that show polarity in a particular context, but L otherwise. After examining its properties, I propose that the phenomenon is best captured in terms of phonologically conditioned allomorphy (Kiparsky 1994). My proposal asserts that Optimality Theory (Prince & Smolensky 1993) can properly account for the distribution of allomorphs. I will demonstrate that selection of morpheme variants is determined by PARSE-MORPH (Akinlabi 1996), and that Alignment constraints (McCarthy & Prince 1993) and constraint conjunction (Crowhurst & Hewitt 1997) are required to ensure that allomorphs are selected according to their appropriate environments.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".