Reanálise de vogais médias pretônicas com o alinhador forçado MFA
Bibliographic record
Abstract
Sociophonetics combines acoustic analysis with the study of natural speech produced by individuals with different social profiles. A challenge in the field is managing large volumes of data while maintaining the methodological rigor of Phonetics and Sociolinguistics. The Montreal Forced Aligner (McAuliffe et al., 2017) can contribute in this regard, as it automatically segments speech sounds; however, the aligner has not yet been widely tested for Brazilian Portuguese. This study replicates the analyses of Oushiro (2019a, 2019b), who investigated the pronunciation of the vowels /e, o/ (as in “legal,” “morada”) in the speech of Northeastern migrants in São Paulo, examining potential convergence toward the São Paulo variety in a dialect contact situation. The original study used EasyAlign (Goldman, 2011) with manual correction of the vowels. In the present study, the Montreal Forced Aligner is applied to the same data to assess whether the results obtained are equivalent, thereby contributing to the validation of its use in sociophonetic research with Portuguese data.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.027 | 0.008 |
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".