<b>Deixis and information packaging in Russian discourse.</b> By Lenore A. Grenoble Amsterdam & Philadelphia: John Benjamins, 1998. Pp. xvii, 455.
Bibliographic record
Abstract
Reviewed by: Deixis and information packaging in Russian discourse by Lenore A. Grenoble Asya Pereltsvaig Deixis and information packaging in Russian discourse. By Lenore A. Grenoble. Amsterdam & Philadelphia: John Benjamins, 1998. Pp. xvii, 455. In this book the term ‘deixis’ is defined negatively, as those properties of an utterance that cannot be known or understood without reference to the communicative acts in which the utterance plays a part. Deixis is divided into three primary dimensions of time, space, and person which are further extended to the secondary dimensions of information status, those of knowledge, focus, and theme. The term ‘information packaging’ is used to cover the linguistic devices involved in encoding information structure and tracking information flow of the discourse. Deixis plays an important role in information packaging [End Page 177] in that it gives interlocutors cues as to both where a referenced information structure is located within the overall packaging of the discourse and how it fits in with the thematic structure of the discourse. The discourse itself is analyzed as constituted by four interrelated frameworks: the linguistic text, the text setting, the text content, and the participant framework. This study of the role of deixis and information packaging in discourse is based on an analysis of several corpora of Russian spoken and written texts. Russian provides an excellent example for studying information packaging because of its morphological and syntactic properties. For instance, rich inflectional morphology allows for a certain freedom of word order as well as for a higher degree of ellipsis and zero anaphora than in English. Other factors that affect deixis and information packaging in Russian are the morphological expression of verbal aspect, the lack of deictic motion verbs, and the absence of definite articles. The book is divided into three parts. The first part presents the framework used throughout the book, focusing on the study of primary temporal, spatial, and person deixis. Ch. 1 defines deixis and its different dimensions. In Ch. 2, the author presents an overview of the primary deictic system of Russian. The second part of the book provides analyses of the use of deixis in organizing the text. Ch. 3 considers the mapping of spatial and temporal dimensions onto the linguistic text, showing how extralinguistic devices are incorporated into the linguistic text. Ch. 4 discusses the issues related to knowledge and evidentiality. Such linguistic devices as reported speech, quotative particles, and evidential adverbs are investigated. The final part explores larger issues of information packaging within the discourse. Ch. 5 examines devices that mark changes in topical flow of the discourse and track the participants, including topicalization, verbal morphology, anaphora, quasi-clefts, and left-dislocation. Ch. 6 focuses on issues of grounding and saliency. Here, the author shows that predicate properties (such as tense, aspect, and mood), as well as individuation properties (such as animacy, person, number, and definiteness) are important in determining information packaging within the discourse. The role of voice and transitivity is likewise discussed in this chapter. Ch. 7 presents the author’s concluding remarks. The book contains name and subject indexes as well as an appendix with some representative texts from the corpora that the study is based on. Asya Pereltsvaig McGill University Copyright © 2001 Linguistic Society of America
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.023 | 0.014 |
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".