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Record W1973172443 · doi:10.1353/lan.0.0076

Analysing sociolinguistic variation. By Sali A. Tagliamonte. (Key topics in sociolinguistics.) Cambridge: Cambridge University Press, 2006. Pp. xi, 284. ISBN 100521778182. $34.99.

2009· article· en· W1973172443 on OpenAlex

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.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueLanguage · 2009
Typearticle
Languageen
FieldSocial Sciences
TopicLinguistic Variation and Morphology
Canadian institutionsnot available
Fundersnot available
KeywordsSociolinguisticsVariation (astronomy)LinguisticsSociologyPhilosophy

Abstract

fetched live from OpenAlex

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...

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.793
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.012
GPT teacher head0.275
Teacher spread0.263 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it