Bibliographic record
Abstract
Measures of vowel overlap to explore the acoustic similarity between proposed and existing vowel categories. They typically compare F1 and F2, and sometimes duration. In the present study, we investigate four methods of quantifying vowel overlap: the spectral overlap assessment metric (Wassink, 2006), the a posteriori probability-based metric (Morrison, 2008), the vowel overlap assessment with convex hulls method (Haynes and Taylor, 2014), and the Pillai score as used by Hay et al. (2006). Based on the data for /i/ and /ɪ/ in the dataset of Hillenbrand et al. (1995), we used Monte Carlo style simulations and repeated subsampling to assess each method. We examined both the two-dimensional (F1 and F2) and three-dimensional (F1, F2, and duration) versions of the methods. We took the methods’ outputs as accurate if they produced values close to expected target values for each type of simulation, and we took the results as precise if there was little spread among the output values. The results suggest that the a posteriori probability-based metric is the most generally applicable, while the Pillai score should be used in scenarios where sensitivity to complete overlap is needed or where data cannot be said to be normally distributed.
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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 | 0.013 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".