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 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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".