Towards Modeling Second Dialect Speech Learning: The Production of Bogota [s] in Ciudad Bolivar by Speakers of Three Different Varieties of Colombian Spanish
Bibliographic record
Abstract
This study investigates the second dialect production of Bogota Spanish /s/ in coda position by speakers of three different varieties of Colombian Spanish, who have been in contact in Ciudad Bolivar, a community located in Bogota, Colombia. The study has three aims. First, it will examine the role of phonetic distance in the acquisition of /s/ production. Second, it will determine the linguistic factors that constrain the realization of /s/ sound by the speakers of the three varieties studied. Third, it will look into the role of extralinguistic factors in the production of /s/. A total of 2322 tokens extracted from sociolinguistic interviews with 50 participants were acoustically analyzed in PRAAT. Statistical analyses were conducted using GoldVarb. The results showed the highest rate of [s] was produced by the speakers of the Eastern Andean variety, followed by the Western Andean, and then by the Coastal variety, suggesting that first dialect phonological processes may affect the acquisition of second dialect sounds. Consistent with previous studies that have examined /s/ variation and change, the linguistic factors position in the word, following segment, and syllable stress were also predictors of /s/ in second dialect production. The extralinguistic factors of age of arrival, age, and gender also had a significant effect on /s/ production in this study. Implications are discussed for models of second dialect speech learning.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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 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".