Analysing sociolinguistic variation. By Sali A. Tagliamonte. (Key topics in sociolinguistics.) Cambridge: Cambridge University Press, 2006. Pp. xi, 284. ISBN 100521778182. $34.99.
Bibliographic record
Abstract
Reviewed by: Analysing sociolinguistic variation Robert Bayley Analysing sociolinguistic variation. By Sali A. Tagliamonte. (Key topics in sociolinguistics.) Cambridge: Cambridge University Press, 2006. Pp. xi, 284. ISBN 100521778182. $34.99. The quantitative analysis of linguistic variation has long been a central concern of sociolinguistics. For many years following the publication of Cedergren and Sankoff's (1974) seminal article on variable rules, however, knowledge of variation analysis was, as Guy observed, 'primarily . . . transmitted by word-of-mouth, an academic version of the preliterate tradition of oral history' (1988:124). Happily, that state of affairs no longer obtains as the field now has a number of guides to both the practical and the technical details of variation analysis. These include Young and Bayley's (1996) guide to the versions of the VARBRUL programs available in the mid 1990s, Bayley's (2002) overview of the principles of variation analysis, and Paolillo's (2002) detailed explanation of the statistical basis of VARBRUL. To this very partial listing, we can now add Sali Tagliamonte's highly accessible step-by-step guide to analyzing variation, from selecting participants and collecting the data through transcription and coding to performing the actual analysis and finally to interpreting the results. T illustrates these steps with variables selected from her own very extensive British and Canadian corpora. The volume, directed toward advanced undergraduate and graduate students, is divided into twelve chapters and also offers considerable online material. Ch. 1 provides an overview of the relationship between linguistics and sociolinguistics, and more specifically, between variationist sociolinguistics as practiced by William Labov and many others (among them, of course, T herself) and other areas of sociolinguistic inquiry such as the ethnography of communication and the sociology of language. T views the dual focus of variationist sociolinguistics on linguistic and social structure as the discipline's defining characteristic. The chapter includes brief discussions of key sociolinguistic concepts including the 'vernacular', the speech community, form/function asymmetry, linguistic variables, the quantitative method, the principle of accountability, circumscribing the variable context (or defining the envelope of variation, that is, what counts as an instance of the variable under investigation?), and hypothesis testing. Chs. 2 and 3 deal with data collection. Ch. 2 concentrates on sampling, with separate sections on the random sampling that characterized early sociolinguistic research (e.g. Labov 1966), social [End Page 900] networks, and the stratified random samples that are common in sociolinguistic research today. T provides examples of the success of the 'friend of a friend' method from her own fieldwork in the African Nova Scotian communities she studied with Shana Poplack (Poplack & Tagliamonte 1991). In Ch. 3, T goes to the heart of data collection—the sociolinguistic interview, a set of procedures designed to elicit the maximum amount of relatively informal speech. T emphasizes thorough preparation, including gaining familiarity with the concerns of the community under study. Managing the substantial amount of data that is necessary for even a relatively small-scale investigation of a speech community is one of the challenges that all researchers face. In Ch. 4, T deals with the practical issues of transcription and corpus construction. Deciding on the level of detail for initial transcription is among the more difficult questions covered in this chapter. T recommends transcribing in standard orthography with standard punctuation, unless the researcher has a compelling reason for doing otherwise. The selection of a linguistic variable is covered in Ch. 5. T provides a variety of examples of possible variables, using data from her York, England corpus as well as data from Ontario. The variables include the classic cases of alveolarization of -ing and coronal stop deletion as well as the quotative be like. In fact, she uses a single excerpt from the York corpus to illustrate the presence of sixteen variables at the phonological, morphosyntactic, and discourse/pragmatic levels, any one of which might provide a suitable topic for investigation. Ch. 6 deals with the most labor-intensive phase of any variationist study—constructing hypotheses and coding the data to test the hypotheses the researcher has developed. As in the previous chapters, T provides effective illustrations, using as examples variable -t,d deletion and variable -ly use. Chs. 7–10 provide...
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.012 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.010 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.106 | 0.073 |
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".