Bibliographic record
Abstract
Frequently cited as a model of sociolinguistic enquiry into language shift and code-switching is John Gumperz's Discourse Strategies (1982): there is hardly a textbook or an article dealing with these subjects which does not refer to the book with approval, if not with reverence.Some of the data cited by Gumperz are from the small, partly bilingual (SlovenelGerman) town of Ziljska BistricalFeistritz an der Gail in Carinthia, Austria Ihenceforward, ZB]; these data are to a significant degree incorrect.Here I correct the errors in the data; elsewhere (Priestly, in prep.)I set the record straight with respect to Gumperz's description of the sociolinguistic situation in ZB and discuss the import of the inaccuracies in that description, and of the errors in his data, on his conclusions with respect to language-shift and to code-switching.This is important: both his description and his data are taken at face value and accepted as accurate by the linguistic public; the fact that they are not accurate, and the extent to which they are not so, may affect his reliability and reputation as one of the luminaries of American sociolinguistics.In three standard collections of articles on codeswitching -Heller (1988), Eastman (1992) and Milroy and Muysken (1995) -Gumperz is cited as an authority very frequently, and the citations are by well-known code-switching specialists such as Shana Poplack, Peter Auer, Monica Heller and Carol Myers-Scotton, and by reliable sociolinguists such as Kathryn Woolard, Susan Gal, and Lesley Milroy.The last-named, in her two important books of 1980 and 1987, cites Gumperz's work in Austria as exemplary in two respects: both his demonstration of the relevance of analysis of change in linguistic network patterns, and his methodology.Moreover, Ronald Wardhaugh, in his well-known and thrice-reprinted sociolinguistic textbook ( 2004), who cites Gumperz as an authority on code-switching and on shared knowledge not only of codes but of their application, accepts as accurate Gumperz's inaccurate description of the verbal repertoire in ZB.So too, Lenore Grenoble (1995), in a laudable exhortation to Slavic linguists to practise sociolinguistics (something they had been very slow to do) quotes Gumperz to exemplify code-switching; and she chooses two examples from his data from ZB, one of which is linguistically inept.Wardhaugh and Grenoble had, unfortunately, no reason not to accept these data.Even when Myers-Scotton (1993: 46, 51, 55) criticizes certain aspects of Gumperz's work, she nevertheless writes that he was "the most influential figure in discussions of the social motivations for Icode-switching] in the 1970s and 1980s;" that his 1982 book was "referred to more widely than the work of any other practitioner writing in the 1980s;" and "ITlhere is no question of Gumperz's extremely positive *My sincere thanks to my two informants from Ziljska Bistrica, and to the meticulous anonymous reviewer who made many very useful comments.
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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 | 0.006 | 0.124 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.021 | 0.036 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.030 | 0.021 |
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".