Spanish nasal assimilation revisited: A cross-dialect electropalatographic study
Bibliographic record
Abstract
Abstract This study employs electropalatography to investigate the implementation of nasal assimilation in two Spanish dialects (Argentinian and Cuban) that differ in the realization of word-final nasals as alveolar or velar. 5 speakers of Argentian and 3 speakers of Cuban Spanish were presented with various utterances containing nasals followed by labial, coronal, and dorsal stops and fricatives under two stress conditions. Results revealed that place assimilation of nasals was consistently accompanied by stricture assimilation. The process was generally categorical, that is, the final alveolar or velar nasal adopted the articulation of the following consonant. Nasal + fricative sequences, however, showed a somewhat different behavior: occasional blocking of nasal assimilation before non-coronals, consistent gradient nasal assimilation before coronals (Argentinian), or categorical/gradient strengthening of post-nasal obstruents (Cuban). Overall, the results are largely consistent with Honorof's (Articulatory gestures and Spanish nasal assimilation, Yale University Ph.D. dissertation, 1999) study of Peninsular Spanish and together provide evidence for dialect-specific grammars of assimilation, which nevertheless share certain general principles of gestural organization.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".