Advancements of phonetics in the 21st century: Quantitative data analysis
Bibliographic record
Abstract
Phonetic research in the 21st century has relied heavily on quantitative analysis. This article reviews the evolution of common practices and the emergence of newer techniques. Using a detailed literature survey, we show that most work follows a mainstream, which has shifted from ANOVAs to mixed-effects regression models over time. Alongside this mainstream, we highlight the increasing use of a diverse methodological toolbox, especially Bayesian methods and dynamic methods, for which we provide comprehensive reviews. Bayesian methods, as well as frequentist methods beyond linear and logistic regression, offer flexibility in model specification, interpretation, and incorporation of prior knowledge. Dynamic methods, such as GAMs and functional data analysis, capture non-linear patterns in acoustic and articulatory data. Machine learning techniques, such as random forests, expand the questions and types of data phoneticians can analyze. We also discuss the growing importance of open science practices promoting replicability and transparency. We argue that the future lies in a diverse methodological toolbox, with techniques chosen based on research questions and data structure.
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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 | 0.068 | 0.166 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.011 | 0.012 |
| Science and technology studies | 0.002 | 0.016 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".