How Much Data Does Linguistic Theory Need? On the Tolerance Principle of Linguistic Theorizing

Yang's (2016) Tolerance Principle describes with incredible precision how many exceptions the mechanisms of child language acquisition can tolerate to induce a productive rule, and is a notable advance in the long-standing controversy as to the amount of data necessary for the acquisition of la...

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Main Author: José-Luis Mendívil-Giró
Format: Article
Language:English
Published: Frontiers Media S.A. 2019-01-01
Series:Frontiers in Communication
Subjects:
Online Access:https://www.frontiersin.org/article/10.3389/fcomm.2018.00062/full
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spelling doaj-f10e90cccce141fc90807388c7d95f5b2020-11-25T02:01:56ZengFrontiers Media S.A.Frontiers in Communication2297-900X2019-01-01310.3389/fcomm.2018.00062435407How Much Data Does Linguistic Theory Need? On the Tolerance Principle of Linguistic TheorizingJosé-Luis Mendívil-GiróYang's (2016) Tolerance Principle describes with incredible precision how many exceptions the mechanisms of child language acquisition can tolerate to induce a productive rule, and is a notable advance in the long-standing controversy as to the amount of data necessary for the acquisition of language. The present contribution addresses a different but related issue, that of the amount of data on variation in languages needed by a linguist to develop a theory of language. Using as a model the perennial question of how many languages should be considered to formulate a general theory of language, I will show that discussions about the type and amount of data needed for linguistic theorizing cannot be fruitful without taking into account the type of linguistic theory and its goals. Moreover, the type of linguistic theory itself depends on the way in which the object of study is conceived. I propose that the two main types of current linguistic theory (functionalism and formalism) correlate broadly to different scientific methods: the inductive one (which proceeds from languages to language) and the deductive one (which proceeds from language to languages), respectively. My aim is to show that the type of data that can falsify a certain linguistic theory is different depending on whether the theory is deductive or inductive. That is, the two types of theory have a different “tolerance threshold” regarding the sparseness of data. Hence, the expectation of progress that new sources of data on language variation can provide for linguistic theory should be modulated according to the objectives and assumptions of each language theory.https://www.frontiersin.org/article/10.3389/fcomm.2018.00062/fulllinguistic theoryBig Datalanguage variationlanguage typologydeductive modelinductive model
collection DOAJ
language English
format Article
sources DOAJ
author José-Luis Mendívil-Giró
spellingShingle José-Luis Mendívil-Giró
How Much Data Does Linguistic Theory Need? On the Tolerance Principle of Linguistic Theorizing
Frontiers in Communication
linguistic theory
Big Data
language variation
language typology
deductive model
inductive model
author_facet José-Luis Mendívil-Giró
author_sort José-Luis Mendívil-Giró
title How Much Data Does Linguistic Theory Need? On the Tolerance Principle of Linguistic Theorizing
title_short How Much Data Does Linguistic Theory Need? On the Tolerance Principle of Linguistic Theorizing
title_full How Much Data Does Linguistic Theory Need? On the Tolerance Principle of Linguistic Theorizing
title_fullStr How Much Data Does Linguistic Theory Need? On the Tolerance Principle of Linguistic Theorizing
title_full_unstemmed How Much Data Does Linguistic Theory Need? On the Tolerance Principle of Linguistic Theorizing
title_sort how much data does linguistic theory need? on the tolerance principle of linguistic theorizing
publisher Frontiers Media S.A.
series Frontiers in Communication
issn 2297-900X
publishDate 2019-01-01
description Yang's (2016) Tolerance Principle describes with incredible precision how many exceptions the mechanisms of child language acquisition can tolerate to induce a productive rule, and is a notable advance in the long-standing controversy as to the amount of data necessary for the acquisition of language. The present contribution addresses a different but related issue, that of the amount of data on variation in languages needed by a linguist to develop a theory of language. Using as a model the perennial question of how many languages should be considered to formulate a general theory of language, I will show that discussions about the type and amount of data needed for linguistic theorizing cannot be fruitful without taking into account the type of linguistic theory and its goals. Moreover, the type of linguistic theory itself depends on the way in which the object of study is conceived. I propose that the two main types of current linguistic theory (functionalism and formalism) correlate broadly to different scientific methods: the inductive one (which proceeds from languages to language) and the deductive one (which proceeds from language to languages), respectively. My aim is to show that the type of data that can falsify a certain linguistic theory is different depending on whether the theory is deductive or inductive. That is, the two types of theory have a different “tolerance threshold” regarding the sparseness of data. Hence, the expectation of progress that new sources of data on language variation can provide for linguistic theory should be modulated according to the objectives and assumptions of each language theory.
topic linguistic theory
Big Data
language variation
language typology
deductive model
inductive model
url https://www.frontiersin.org/article/10.3389/fcomm.2018.00062/full
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