The phonetic rhythm/syntax headedness connection: Evidence from Tagalog
Bibliographic record
Abstract
Ramus, Nespor, and Mehler [Cognition (1999)] show that the rhythm of a language (broadly: stress- versus syllable- versus mora-timing) results from the proportion of vocalic material in an utterance (%V) and the standard deviation of consonantal intervals (delta-C). Based on 14 languages, Shukla, Nespor, and Mehler [submitted] further argue that rhythm is correlated with syntactic headedness: low %V is correlated with head-first languages (e.g., English); high %V is correlated with head-final languages (e.g., Japanese). Together, these proposals have important implications for language acquisition: infants can discriminate across rhythm classes [Nazzi, Bertoncini, and Mehler, J. Exp. Psych: Human Perception and Performance (1998)]. If rhythm, as defined by %V and delta-C, can predict headedness, then infants can potentially use rhythm information to bootstrap into their languages syntactic structure. This paper reports on a study analyzing rhythm in a language not yet considered: Tagalog. Results support the Shukla et al. proposal in an interesting way: based on its %V and delta-C, Tagalog falls between head-first and head-last languages, slighty closer to the head-first group. This placement correlates well with the fact that, although Tagalog is said to be primarily head-first syntactically, head-last phrases are permitted and common in the language.
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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 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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 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".