Building Coherence and Cohesion: Task-Oriented Dialogue in English and Spanish (review)
Bibliographic record
Abstract
Reviewed by: Building Coherence and Cohesion Joyce Bruhn De Garavito (bio) María Teresa Taboada . Building Coherence and CohesionJohn Benjamins. xiv, 262. US $138.00 This book offers a detailed examination of how coherence and cohesion are built in oral conversations. It focuses on task-oriented speech, namely scheduling dialogues, in two languages: Spanish and English. The data are not fully naturalistic, because they are part of experimental research carried out by Carnegie Mellon University and the University of Pittsburgh, originally designed to study machine translation possibilities. Pairs of speakers of each language were asked to try to negotiate a date and time in which they could meet. The speakers could not see each other, and the dialogues were recorded. The result, to judge by the examples, seems very natural. The book comprises eight chapters and an appendix. After a brief introduction in the first chapter, the author delves into a detailed examination of the theoretical issues related to the application of frameworks and methodologies developed primarily for written samples to the analysis of oral conversation. In fact, one of the strengths of the book is the in-depth review of the literature, spanning not only the field of genre analysis, but also cultural studies, linguistics, second language acquisition, and others. The third chapter describes the data, including the participants, the task, and the type of coding used. The results are summarized in the following four chapters, with chapter 4 focusing on the thematic structure of the dialogues, chapter 5 on the rhetorical relations, chapter 6 on the development of cohesion, and chapter 7 on coherence. Chapter 8 summarizes the conclusions. In this review I will focus on the comparison of the results for the two languages, because I found them interesting given the issues that they raise regarding the analysis. The author leans to a structural definition of Theme, as the first element or point of departure of the message. The problem, as she points out, is that in English subjects are obligatory, while Spanish is a Pro-drop language. It is easy to predict, therefore, that in English the point of departure is often going to be the Participant subject while in Spanish it is going to be a Process. This difference falls out naturally from the grammatical structure of the language, and one may wonder what new information is gathered from a Thematic analysis of this type that cannot be deduced from the purely syntactic analysis. This problem may be more serious if we look at typologically distant languages. The other main difference between the languages was found in the types of cohesion devices used. English speakers favoured substitutions, while Spanish speakers favoured ellipsis. Again, this seems to be the result of the structure of the two languages involved. The final difference is, I think, more related to cultural differences than syntactic ones. In the development of the Progression sequences of the dialogues (opening, proposal/discussion, closing), the closing section of the dialogues was slightly longer for the Spanish speakers. It is possible to predict longer openings also, but, as the author points out, the speakers had already talked when [End Page 185] the experiment started, so it is possible they did not feel the need to extend the introductory segments. This book is a very valuable contribution to the field. It is remarkable how easily Taboada applies the concepts of genre analysis to oral production. Her analysis is detailed and careful. The frequent use of examples from the data guides the reader towards a clear understanding of how these conversations have been put together, the glue that makes them cohesive and coherent. This is an important book not only for those interested in the structure of conversations per se, but also in particular for second-language teachers, who can benefit from such an in-depth understanding of what makes conversations in different languages work. [End Page 186] Joyce Bruhn De Garavito Joyce Bruhn de Garavito, Department of Spanish, University of Western Ontario Copyright © 2006 University of Toronto Press Incorporated
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.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".