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Hard-Science Linguistics This page intentionally left blank Hard-Science Linguistics Edited byVictor H. Yngvean.
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- Linguistic inequality in scientific communication today
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Linguistics/Introduction - Wikibooks, open books for an open world
Overview Your benefits Versatile platform for research exchange and discussion Cutting edge results of theoretical linguistic research Critical evaluation of theories and approaches Outstanding authors and reviewers Internationally recognized editorial board. Objective The Linguistic Review publishes high-quality papers in phonology, morphology, syntax, semantics and pragmatics, within a framework of Generative Grammar and related disciplines, as well as critical discussions of theoretical linguistics as a branch of cognitive psychology.
The Linguistic Review is a peer-reviewed journal of international scope. Topics Pragmatics Cognitive Science Syntax Semantics Phonology Morphology Generative Grammar and related disciplines Theoretical linguisticsCognitive psychology Article formats Reviews of important new monographs, dissertation abstracts, and letters to the editor Information on Submission Process.
Instructions for Authors Submission Please send an anonymous electronic version PDF format of the article manuscript in one file as well as an anonymous abstract max. Authors will receive PDF proofs for correction which must be returned by dates given in the publication schedule Send it directly to The Linguistic Review editorial office the. Submissions are sent to at least 2 reviewers Decision on your paper Once accepted for publication, authors are requested to submit the final version in the form of two hard copies sent to the Editor-in-Chief and, in addition, an attachment non-PDF , conforming to journal style.
If possible, one of the common word processing systems or LATEX should be used Ahead of print publishing Upon publication, authors will receive electronic offprints in PDF format of their contribution. This radical position is hardly sustainable in practice. It is among its ranks that linguists like me, dissatisfied with the practice of using themselves as informants, have turned to corpora, judging them to be a far better source than introspection to test their hypotheses. Admittedly, corpora have their limitations. The most frequent criticism that generative linguists level against corpus linguists is that no corpus can ever provide negative evidence.
In other words, no corpus can indicate whether a given sentence is impossible. Corpus linguists reply that grammar rules are generalizations over actual usage and negative evidence is of little import.
Linguistic inequality in scientific communication today
The second response is that there are statistical methods that can either handle the non-occurrence of a form in a corpus or estimate the probability of occurrence of a yet unseen unit. A more serious criticism is the following: no corpus, however large and balanced, can ever hoped to be representative of a speaker, let alone of a language. In an interview Andor, , p. But this is a matter of theoretical faith. Traditional corpus linguistics does not claim to account for a language, let alone the language faculty, it does account to a certain form of competence.
It just not the same kind of competence see above. Corpus linguistics is not about experimentation; it is about observation. Observation is an essential component of the hard sciences. The universe is by essence infinite. Only an infinitesimal portion of it can be observed. The strength of the hard sciences is their ability to extend the conclusions based on the observation of phenomena in a finite sample to a much larger, possibly infinite sample.
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First-generation corpus linguistics has to refrain from extending conclusions beyond the investigated corpus. The linguist would be well-advised to refrain from making any conclusion about British English because, as a sample, the corpus is biased and certainly not fully representative. The linguist cannot extend her corpus-based findings to the language unless she backs them up with solid inferential statistics see this paper I wrote with Antoine Chambaz. This is where second-generation corpus linguistics, augmented with inferential statistics, steps in.
It allow us to extend the conclusions obtained from a finite sample a corpus to a larger sample a language in a controlled fashion. In a way, corpus linguistics is moving closer to physics and chemistry, and it is not just about collecting videotapes of things happening in language.
If you want to know what happened next, click on play. Most of my colleagues in psycholinguistics would argue otherwise. Elegantly, Chomsky connects his reply to some examples he provided earlier in his lecture. Of course, data does something. So some things are clearly learned. And more is learned than that. So take river again. Like in English, there is a difference between river, stream, creek, you know, a couple of other things.
Other languages may not have exactly those differences, they have some other set of differences. So in what used to be called, should be called, semantic fields, you know, domains in which a group of words roughly fit, the study made by a German linguist years ago, within the semantic fields you get the semantic organization of the field.
You have to learn in English that a certain thing is a river and another thing is a stream, not a river. Some other language may not make that distinction, or may make some other distinction. Now this was shown by Ken Hale pretty strikingly in some papers back in the s in studying a wide range of Australian Aboriginal languages. According to him, words do not provide access to the conceptual structure because some concepts are known, but not necessarily encoded in the form of a dedicated word.
In other words, usage cannot provide access to competence. Just because a concept is not lexicalized does not mean that it cannot be expressed verbally. Even if a concept is not lexicalized, corpus linguists can still hope to capture it indirectly.
In : Intercultural Pragmatics 1. In : Journal of Causal Inference 4. Site web.
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