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Sources of Microtemporal Clustering in Sociolinguistic Sequences
Persistence is the tendency of speakers to repeat the choice of sociolinguistic variant they have recently made in conversational speech. A longstanding debate is whether this tendency toward repetitiveness reflects the direct influence of one outcome on the next instance of the variable, which I ca...
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Frontiers Media S.A.
2019
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7861327/ https://www.ncbi.nlm.nih.gov/pubmed/33733099 http://dx.doi.org/10.3389/frai.2019.00010 |
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author | Tamminga, Meredith |
author_facet | Tamminga, Meredith |
author_sort | Tamminga, Meredith |
collection | PubMed |
description | Persistence is the tendency of speakers to repeat the choice of sociolinguistic variant they have recently made in conversational speech. A longstanding debate is whether this tendency toward repetitiveness reflects the direct influence of one outcome on the next instance of the variable, which I call sequential dependence, or the shared influence of shifting contextual factors on proximal instances of the variable, which I call baseline deflection. I propose that these distinct types of clustering make different predictions for sequences of variable observations that are longer than the typical prime-target pairs of typical corpus persistence studies. In corpus ING data from conversational speech, I show that there are two effects to be accounted for: an effect of how many times the /ing/ variant occurs in the 2, 3, or 4-token sequence prior to the target (regardless of order), and an effect of whether the immediately prior (1-back) token was /ing/. I then build a series of simulations involving Bernoulli trials at sequences of different probabilities that incorporate either a sequential dependence mechanism, a baseline deflection mechanism, or both. I argue that the model incorporating both baseline deflection and sequential dependence is best able to produce simulated data that shares the relevant properties of the corpus data, which is an encouraging outcome because we have independent reasons to expect both baseline deflection and sequential dependence to exist. I conclude that this exploratory analysis of longer sociolinguistic sequences reflects a promising direction for future research on the mechanisms involved in the production of sociolinguistic variation. |
format | Online Article Text |
id | pubmed-7861327 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-78613272021-03-16 Sources of Microtemporal Clustering in Sociolinguistic Sequences Tamminga, Meredith Front Artif Intell Artificial Intelligence Persistence is the tendency of speakers to repeat the choice of sociolinguistic variant they have recently made in conversational speech. A longstanding debate is whether this tendency toward repetitiveness reflects the direct influence of one outcome on the next instance of the variable, which I call sequential dependence, or the shared influence of shifting contextual factors on proximal instances of the variable, which I call baseline deflection. I propose that these distinct types of clustering make different predictions for sequences of variable observations that are longer than the typical prime-target pairs of typical corpus persistence studies. In corpus ING data from conversational speech, I show that there are two effects to be accounted for: an effect of how many times the /ing/ variant occurs in the 2, 3, or 4-token sequence prior to the target (regardless of order), and an effect of whether the immediately prior (1-back) token was /ing/. I then build a series of simulations involving Bernoulli trials at sequences of different probabilities that incorporate either a sequential dependence mechanism, a baseline deflection mechanism, or both. I argue that the model incorporating both baseline deflection and sequential dependence is best able to produce simulated data that shares the relevant properties of the corpus data, which is an encouraging outcome because we have independent reasons to expect both baseline deflection and sequential dependence to exist. I conclude that this exploratory analysis of longer sociolinguistic sequences reflects a promising direction for future research on the mechanisms involved in the production of sociolinguistic variation. Frontiers Media S.A. 2019-06-20 /pmc/articles/PMC7861327/ /pubmed/33733099 http://dx.doi.org/10.3389/frai.2019.00010 Text en Copyright © 2019 Tamminga. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Artificial Intelligence Tamminga, Meredith Sources of Microtemporal Clustering in Sociolinguistic Sequences |
title | Sources of Microtemporal Clustering in Sociolinguistic Sequences |
title_full | Sources of Microtemporal Clustering in Sociolinguistic Sequences |
title_fullStr | Sources of Microtemporal Clustering in Sociolinguistic Sequences |
title_full_unstemmed | Sources of Microtemporal Clustering in Sociolinguistic Sequences |
title_short | Sources of Microtemporal Clustering in Sociolinguistic Sequences |
title_sort | sources of microtemporal clustering in sociolinguistic sequences |
topic | Artificial Intelligence |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7861327/ https://www.ncbi.nlm.nih.gov/pubmed/33733099 http://dx.doi.org/10.3389/frai.2019.00010 |
work_keys_str_mv | AT tammingameredith sourcesofmicrotemporalclusteringinsociolinguisticsequences |