Los acentos tonales ascendentes en el español de Santiago de Chile
Bibliographic record
Abstract
En el modelo metrico y autosegmental (AM) se asocian el texto y el tono (Ladd, 1996: 42; Hualde, 2003). La asociacion fonologica es primaria y se realiza en un unico nivel: en el nivel de las silabas acentuadas (Pierrehumbert, 1980; Pierrehumbert y Beckman, 1988; Ladd, 1996; Beckman et al. 2002; Beckman et al. 2005). En cada acento tonal se actualizan uno o dos tonos: uno, central, alineado con la duracion de la silaba acentuada y otro, periferico, en la silaba pretonica o en el inicio de la silaba acentuada o en la silaba postonica (Pierrehumbert, 1980: 25; Ladd, 1996: 79). La centralidad del tono en el acento tonal se indica por el diacritico estrella: H*, L* (Ladd, 1976: 79). En la secuencia LH (bajo, alto) o en la secuencia HL (alto, bajo), en el desarrollo temporal de la silaba acentuada y en el contexto pretonico y postonico, se actualizan diferentes alineaciones foneticas segun el tiempo de realizacion. De este modo, se producen distintas asociaciones fonologicas y distintas formalizaciones. Este nivel formal de los acentos tonales constituye la taxonomia propia de cada dialecto y dentro de cada lengua: indica el contraste linguistico, especificamente, las relaciones pragmaticas (Hualde, 2003). Para el espanol, el analisis formal da como resultante dos acentos tonales monotonales (H*, L*) y cuatro acentos bitonales (L*+H, L+H*, H+L*, H*+L) (ver la Fig. 1, basado en Hualde, 2003).
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".