Remote sociophonetic data collection: Vowels and nasalization from self‐recordings on personal devices
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract When the COVID‐19 pandemic halted in‐person data collection, many linguists adopted modern technologies to replace traditional methods, including speaker‐led options in which participants record themselves using their own personal computers or smartphones and then email or upload the sound files to online storage sites for researchers to retrieve later. This study evaluated the suitability of such ‘home‐made’ recordings for phonetic analysis of vowel space configurations, mergers, and nasalization by comparing simultaneous recordings from several popular personal devices (Macbook, PC laptop, iPad, iPhone and Android smartphone) to those taken from professional equipment (H4n field recorder, Focusrite with Audio Technica 2021 microphone). All personal devices conveyed vowel arrangements and nasalization patterns relatively faithfully (especially laptops), but absolute measurements varied, particularly for the female speaker and in the 750–1500 Hz range, which affected the locations (F1 × F2) of low and back vowels and reduced nasalization measurements (A1−P0) for the female's pre‐nasal vowels. Based on these results, we assess the validity of remote recording using these consumer devices and offer recommendations for best practices for collecting high fidelity acoustic phonetic data from a distance.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it