<b>The minimalist parameter.</b> Selected papers from the Open Linguistics Forum, Ottawa, 21-23 March 1997. Ed. by Galina M. Alexandrova and Olga Arnaudova. (Current issues in linguistic theory 192.) Amsterdam & Philadelphia: John Benjamins, 2001. Pp. 360. $87.00.
Bibliographic record
Abstract
Reviewed by: Language, text and knowledge: Mental models of expert communication ed. by Lita Lundquist, Robert J. Jarvella. Natalie Sciarini-Gourianova Language, text and knowledge: Mental models of expert communication. Ed. by Lita Lundquist and Robert J. Jarvella. Berlin & New York: Mouton de Gruyter, 2000. Pp. 326. $93.35. It has recently become clear that almost all the cognitive sciences share the same aim: to explain how language and knowledge interact when people create, use, and understand texts. This theoretical approach unites all the works presented in this volume. Linguists, psycholinguists, and psychologists try to show how this interaction of language and knowledge works within a specific domain of expert knowledge, that is, how language is used in real expert situations. The volume consists of twelve papers, organized according to different levels of analysis: from words and phrases, to sentences and cross-sentential relations, to argumentation, texts, and knowledge domains. All the papers combine a data-oriented approach with certain methodologies borrowed from linguistics, text linguistics, translation theory, experimental psychology, and psycholinguistics. The fields of specific knowledge treated here are law, medicine, sociology, economics, and the study of risk. The authors pursue the same goal of investigating the interaction between cognitive aspects of language and text on the one hand and the structure of specialized knowledge on the other. The following examples demonstrate the main framework of the research and the range of presented topics. Åse Almlund uses Charles Fillmore’s semantic case role model (‘The case for case’, in Emmon Bach & Robert Harms (eds.), Universals in linguistic theory, New York: Holt, Rinehart, and Winston, pp. 1–88, 1968) to show how the patient role can supply the reader, both expert and nonexpert, with important information about the structuring and content of legal texts. Almlund says that problems of text comprehension are obvious obstacles to the inexperienced or semiprofessional translator of legal texts, for such texts contain something that is ‘unspoken’ and thus incomprehensible for a third party. Lita Lundquist aims to show that three types of knowledge (linguistic, text, and world knowledge) can be compiled with one formula, the generalized event model. Lundquist focuses on the phenomenon of trans-sentential NP anaphors, the interpretation of which poses certain problems in specialized texts, because a nonexpert cannot know whether two NPs refer to the same entity. The generalized event model serves as a common denominator between cognitive structures in the lexicon, sentences, texts, and background knowledge. Annely Rothkegel tries to find out what enables a professional translator to first identify culturally-specific information in a text and then replace it with information appropriate for recipients from another culture. She analyzes what is transferred via translation, knowledge, or text and what kind of relationship holds between them and proposes a model of an implicit text plan that brings together speech-act based frames and the dynamic communicative environment of text with more static aspects of a text’s global and local structure. Leo G. M. Noordman, Wietske Vonk, and Wim H. G. Simons present a set of experiments that explore similarities and differences in the structure of economic knowledge of highly educated experts in economics vs. nonexperts. The rich set of data demonstrates differences between these two groups in the sets of concepts they have, in how the concepts or measures relate to one another in familiarity with this kind of information, and in the manner of access to the concepts in mental representations. The results are summarized using network diagrams which show positive and negative casual interconnections among 20 basic concepts. Other contributors to this collection are Pierre-Yves Raccah, Jan Engberg, Henrik Høeg Müller, Anne Lise Kjær, Dorte Madsen, Lene Palsbro, Robert J. Jarvella and Suzie Mathieu, Anthony J. Sanford, and Linda M. Moxey. All the reports prove that the mental models approach can be successfully used in various text studies in the relatively new dimension of specific knowledge and differences in knowledge between experts and novices in a certain field. In general, this...
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.063 | 0.019 |
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".