Melodic constructions in Spanish: Metrical structure determines the association properties of intonational tones
Bibliographic record
Abstract
This paper explores phrase-length-related alternations in the association of tones to positions in metrical structure in two melodic constructions of Spanish. An imitation-and-completion task eliciting (a) the low–falling–rising contour and (b) the circumflex contour on intonation phrases (IPs) of one, two, and three prosodic words revealed that, although the focus structure and pragmatic context is constant across conditions, phrases containing one prosodic word differ in their nuclear (i.e. final) pitch accents and edge tones from phrases containing more than one prosodic word. For contour (a), short intonation phrases (e.g. [ Ma no lo ] IP ) were produced with a low accent followed by a high edge tone (L* H% in ToBI notation), whereas longer phrases (e.g. [ El her ma no de la a m iga de Ma no lo ] IP ‘Manolo's friend's brother’) had a low accent on the first stressed syllable, a rising accent on the last stressed syllable, and a low edge tone (L* L+H* L%). For contour (b), short phrases were produced with a high–rise (L+H* ¡H%), whereas longer phrases were produced with an initial accentual rise followed by an upstepped rise–fall (L+H* ¡H* L%). These findings imply that the common practice of describing the structure of intonation contours as consisting of a constant nuclear pitch accent and following edge tone is not adequate for modeling Spanish intonation. To capture the observed melodic alternations, we argue for clearer separation between tones and metrical structure, whereby intonational tones do not necessarily have an intrinsic culminative or delimitative function (i.e. as pitch accents or as edge tones). Instead, this function results from melody-specific principles of tonal–metrical association.
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 | 0.001 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".