The Routledge Handbook of Historical Linguistics

Bowern, Claire; Evans, Bethwyn

doi:10.4324/9781315794013

Cited by 34 publications

(4 citation statements)

References 37 publications

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“…However, one of the main explanatory factors is the way changes in language, usually small, accumulate, and amplify across time in space, resulting in this astonishing diversity (Evans and Levinson, 2009;Levinson and Evans, 2010;Bowern and Evans, 2014;Dediu et al, 2017). There are currently many proposals that identify various factors shaping language change, ranging from those internal to language (Lass, 1997;Campbell, 1998;Bowern and Evans, 2014), to demography and population movements (Ostler, 2005;Hua et al, 2019), to environmental and ecological factors (Everett et al, 2016;Bentz et al, 2018), and even to the biology and cognition of the language users Wong et al, 2020). However, this enigma cannot be answered without fully embracing the complexity of language itself, "evolving" and "living" at the interface of biology, cognition, society, and culture (Levinson, 2006;Mufwene et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

“…Here, we take a broad cultural evolutionary view of language change (Cavalli-Sforza and Feldman, 1981;Croft, 2008;Richerson and Boyd, 2008;Dediu et al, 2013) in which linguistic variation is first generated through innovation, and then it may spread (or not) through the linguistic community, due to the complex interplay between random factors (akin to drift in evolutionary biology) and various types of selective pressures (or biases). Even though predicting language change (and evolutionary change, in general) is notoriously hard (Stadler, 2016), the mechanisms underlying language change have been the object of intensive study in particular in sociolinguistics (Milroy and Gordon, 2008;Meyerhoff, 2015) and historical linguistics (Bowern and Evans, 2014), but also in phonetics and phonology (Ohala, 1989;Yu, 2013). Of special interest is the so-called "actuation problem" (Weinreich et al, 1968;Yu, 2013;, which can be briefly stated as " [w]hy do changes in a structural feature take place in a particular language at a given time, but not in other languages with the same feature, or in the same language at other times?"…”

Section: Introductionmentioning

confidence: 99%

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Interindividual Variation Refuses to Go Away: A Bayesian Computer Model of Language Change in Communicative Networks

et al. 2021

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Treating the speech communities as homogeneous entities is not an accurate representation of reality, as it misses some of the complexities of linguistic interactions. Inter-individual variation and multiple types of biases are ubiquitous in speech communities, regardless of their size. This variation is often neglected due to the assumption that “majority rules,” and that the emerging language of the community will override any such biases by forcing the individuals to overcome their own biases, or risk having their use of language being treated as “idiosyncratic” or outright “pathological.” In this paper, we use computer simulations of Bayesian linguistic agents embedded in communicative networks to investigate how biased individuals, representing a minority of the population, interact with the unbiased majority, how a shared language emerges, and the dynamics of these biases across time. We tested different network sizes (from very small to very large) and types (random, scale-free, and small-world), along with different strengths and types of bias (modeled through the Bayesian prior distribution of the agents and the mechanism used for generating utterances: either sampling from the posterior distribution [“sampler”] or picking the value with the maximum probability [“MAP”]). The results show that, while the biased agents, even when being in the minority, do adapt their language by going against their a priori preferences, they are far from being swamped by the majority, and instead the emergent shared language of the whole community is influenced by their bias.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Interindividual Variation Refuses to Go Away: A Bayesian Computer Model of Language Change in Communicative Networks

et al. 2021

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“…Nas Figs [46]. Embora seja incontestável que línguas mudam ao longo do tempo, iremos considerar que, em uma análise sincrônica, suas características não se alteram drasticamente e podem ser encontradas em qualquer amostra representativa de uma língua, em um determinado instante [47]. A Fig.…”

Section: Resultsunclassified

Influência da lei de Zipf na escolha de senhas

Araújo

Sansão

Yehia

2016

Rev. Bras. Ensino Fís.

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Este artigo apresenta uma análise sob a ótica de teoria da informação de algumas maneiras de criar senhas para dificultar um ataque por força bruta. A lei de Zipf é observada nas línguas naturais e, por conseguinte, a entropia é reduzida quando as utilizamos ao criar uma senha. Muitas das empresas utilizam a política de restringir o número de caracteres de uma senha. Além disso, queremos criar senhas que não sejam demasiadas longas, nem que sejam difíceis de se memorizar. Mostraremos que, dado este cenário, a melhor estratégia (relação de compromisso entre criar uma senha forte e uma senha de fácil memorização e utilização) é a utilização de acrônimos, senhas formadas pela combinação (aparentemente sem nexo) da primeira letra de palavras em uma frase. Com esta abordagem podemos aumentar em aproximadamente 80% a entropia por caractere.

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“…Proses Meminjam dari sumber asing akan memunculkan pemetaan inovasi antara kata dan konsep dalam suatu sistem linguistik. Dalam proses peminjaman kata asing materi leksikal juga diadaptasi (Bowern & Evans, 2015). Pada kasus pemaknaan kata asing, proses peminjaman dapat mencakup makna dan simbol atau hanya meminjam simbol saja tanpa makna.…”

Section: Konstruksi Maknaunclassified

Tipologi Kontruksi Makna Kata “Taliban” Pada Komisi Pemberantasan Korupsi (KPK)

Ilyas

2020

AlTarib

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The word Taliban has been used by the public and is widely known as the name of the faction of the armed political movement in Afghanistan. However, in the past some time, the term Taliban is often used as a name for a group within the Corruption Eradication Commission (KPK). This raises different meanings and different perceptions. This study aims to analyze how the word Taliban is understood and used in the KPK, how the shape shifts in meaning, and how the construction process patterns. This study uses a descriptive-qualitative approach with data taken from literature and it is enriched with interview and questionnaire data. This study found that the development of meaning was unpredictable but found conclusions in synchronic phenomena when there was a large and open use of words. The common conception and common understanding on the word "Taliban” is doubtful because it can be broadly constructed. The semantic fields used are nouns and adjectives with various meanings. Meaning is constructed from individual perspectives, social and cultural contexts, the it is continued to mutual understanding and natural agreement. Peryoration and amelioration types were the most noticeable patterns of change. However, the positive-positive meaning is also a different pattern found in shifting the meaning of the Taliban in the KPK.

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The Routledge Handbook of Historical Linguistics

Cited by 34 publications

References 37 publications

Interindividual Variation Refuses to Go Away: A Bayesian Computer Model of Language Change in Communicative Networks

Interindividual Variation Refuses to Go Away: A Bayesian Computer Model of Language Change in Communicative Networks

Influência da lei de Zipf na escolha de senhas

Tipologi Kontruksi Makna Kata “Taliban” Pada Komisi Pemberantasan Korupsi (KPK)

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