Bibliographic record
Abstract
As a follow-up to the author’s 2018 analysis of New York City English in film, this article turns its attention to the whole country over the same 80-year period of 1930–2010, using acoustic phonetic, quantitative, and statistical analysis to identify the most important changes in the pronunciation of North American English by 40 European American leading actresses in their best-known films. Focusing mostly on vowel production, the analysis reveals a gradual shift from East Coast patterns rooted in the speech of New York City to West Coast patterns rooted in the speech of Los Angeles. Changes include a decline in /r/ vocalization, which is restricted almost entirely to the period before the mid-1960s; a decline in the low back distinction between /o/ and /oh/ (lot and thought); a new distinction between /æ/ (trap) and its allophone before nasal consonants (e.g., ham or hand); shifts of /æ/ and /oh/ to a lower, more central position in the vowel space; and fronting of the back upgliding vowel /uw/ (goose). These and other patterns correspond closely to those identified in the speech of ordinary people, revealing an intriguing parallel between public speech in the mass media and private speech in local communities.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.666 | 0.548 |
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".