<b>Einführung in die allgemeine Sprachwissenschaft.</b> By August Dauses. Stuttgart: Franz Steiner Verlag, 1997. Pp. 123.
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.
Bibliographic record
Abstract
Reviewed by: Spontaneous spoken language: Syntax and discourse by Jim Miller, Regina Weinert Jennifer Dailey-O’Cain Spontaneous spoken language: Syntax and discourse. By Jim Miller and Regina Weinert, Oxford: Clarendon Press, 1998. Pp. 457. In linguistics, where the theoretical subfields do not often draw on natural data and the data-oriented [End Page 613] subfields have thus far failed to contribute much to theory, along come Jim Miller and Regina Weinert to challenge our assumptions. Spontaneous spoken language based primarily on concrete spoken data from English, German, Russian, and French, but the implications for theory are clear. Their message is a deceptively simple one: Spoken and written language are different and should be treated as such in all branches of the field. This is hardly a new idea, but M & W show how this basic tenet of linguistics has often been ignored by those who espouse it. Chs. 2–4 provide an extensive analysis of various structures of the syntax of spoken language, arguing that based on large amounts of data, the clause should be taken as the central unit of syntax (and the sentence as a low-level discourse unit.) They then go on to illustrate some of the major differences between spoken and written language at the levels of both the clause and the phrase. It is argued that theoretical syntacticians often ignore these differences, using examples which would only be found in written language to support their theories, which are supposed to be based on spoken language. Chs. 5–6 argue for an increased awareness of the differences between spoken and written discourse, first giving a general overview and then concentrating on an analysis of two features of spoken discourse, cleft constructions and like. Finally, the last two chapters argue that spontaneous spoken data have a direct impact on topics occupying a central focus in linguistics, including historical linguistics, typology, the study of first language acquisition, and the definition of standard language. Their points are well-argued and convincing. This book has two main weaknesses. First, the analysis could have benefitted from an equivalent written corpus with which to compare the spoken corpus. Although the findings about spoken language are often compared with written texts (fiction, magazines, and newspapers), evidence that the authors had thoroughly examined a comparable written corpus would have added weight to their arguments. Second, the extensive qualitative analysis might well have been augmented by the use of a complementary statistical analysis, in order to ‘check’ the conclusions quantitatively. The frequent examples also make for somewhat tedious reading at times, though this seems necessary. M & W, close with the stated hope that other language scholars will take up the questions their work raises. I fear that this book will not be widely read because it cannot be placed squarely within one of the subfields of linguistics as we presently conceive them. However,M&W’s ideas provide an important challenge to mainstream linguistics, and the field could clearly benefit from debating them, whether or not one agrees with them. Jennifer Dailey-O’Cain University of Alberta Copyright © 2001 Linguistic Society of America
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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 it