Second Dialect Acquisition “in Real Time”: Two Longitudinal Case Studies from YouTube
Bibliographic record
Abstract
Longitudinal tracking of second dialect acquisition normally requires carefully planned data collection and years of patience. However, the rise of self-recorded public speech data on internet archives such as YouTube affords researchers with a novel way of tracking language change over time. This article presents two case studies of YouTube vloggers who have recorded their voices over the course of a decade (or longer) and have also relocated from different dialect regions of the United States to the West Coast. It reveals that, in addition to typical age-graded change such as a decrease in fundamental frequency over time, some vocalic aspects of their original dialects (Hawai‘i English and Inland North English) shifted to become more in line with Western American English, while others did not. The disparity between the vowels that changed and those that did not for each speaker are discussed through the lenses of social salience, gender and race, and the audience design of YouTube vlogs.
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How this classification was reachedexpand
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 | 0.001 | 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 | 0.001 | 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) | 0.007 | 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 teacher head, 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".