Bibliographic record
Abstract
Pretonic lengthening has been reported as the most salient characteristic of Córdoba Spanish (Lang-Rigal 2014; Lenardón 2017). If the relative duration of the pretonic to the tonic is a cue to stress in Spanish (Ortega & Prieto 2011; Hualde 2015), it is worth exploring what the cues are in a variety in which pretonic syllables are longer than tonic ones. I analyze the production of oxytones, bi- and trisyllabic paroxytones and proparoxytones by six speakers from Córdoba and three from the neighbouring province of San Luis. An analysis of relative duration and intensity, as well as pitch accent types, revealed that pretonic lengthening was more prominent in and near the capital of Córdoba than in the rest of the province but was also attested in San Luis. Consistent with previous studies (Requena et al. 2013; Lang-Rigal 2014), participants from Córdoba showed a tendency to align the f0 peak within the stressed syllable but varied in the alignment of the peak depending on the type of word and the location. Overall results support the existence of a geographical continuum in the use of relative duration of tonic, pretonic and posttonic syllables (Vidal de Battini 1964) and reveal the need of more systematic explorations of the correlate of stress in different word types and across Argentine varieties.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.004 | 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".