Compară metode
Examinează metodele selectate una lângă alta; rândurile care diferă sunt evidențiate.
| Apparent-Time Analysis× | Sociophonetic Analysis× | |
|---|---|---|
| Domeniu | Lingvistică | Lingvistică |
| Familie | Process / pipeline | Process / pipeline |
| Anul apariției≠ | 1963 | 2006 |
| Autorul original≠ | William Labov | Sociophoneticians (William Labov; Paul Foulkes; Erik R. Thomas) |
| Tip≠ | Inferential design for detecting language change in progress | Workflow correlating acoustic phonetic measurement with social factors |
| Sursa seminală≠ | Labov, W. (1963). The social motivation of a sound change. Word, 19(3), 273–309. DOI ↗ | Foulkes, P., Scobbie, J. M., & Watt, D. (2010). Sociophonetics. In W. J. Hardcastle, J. Laver, & F. E. Gibbon (Eds.), The Handbook of Phonetic Sciences (2nd ed., pp. 703–754). Wiley-Blackwell. ISBN: 9781405145909 |
| Denumiri alternative | Apparent-Time Construct, Apparent-Time Hypothesis, Age-Stratified Change Analysis | Sociophonetics, Sociophonetic Variation Analysis, Phonetic Variation Analysis |
| Înrudite | 4 | 4 |
| Rezumat≠ | Apparent-time analysis is the foundational variationist method for detecting language change in progress without waiting for time to pass. Introduced by William Labov in his 1963 study of Martha's Vineyard, it compares the speech of speakers of different ages sampled at a single moment and treats the age dimension as a proxy for historical time: if younger speakers use a variant more than older speakers, that age gradient is read as evidence of change unfolding across generations. The inference rests on the apparent-time hypothesis — that an individual's vernacular is largely fixed in adolescence and remains stable through adult life — so that the speech of today's seventy-year-olds reflects the community norms of roughly fifty years ago. | Sociophonetic analysis sits at the intersection of acoustic phonetics and variationist sociolinguistics. It applies the precise, quantitative measurement of phonetic variables — vowel formants, voice onset time (VOT), the spectral moments of /s/, and many others — to socially structured samples of speech, then correlates those measurements with social factors such as age, social class, gender, ethnicity, and region. The result is a fine-grained, statistically defensible account of how phonetic detail carries social meaning and how it patterns across communities and across time, increasingly built on large-scale, automated measurement. |
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