Applying Population Genetic Approaches within Languages
Finnish Dialects as Linguistic Populations
Bibliographic Data
| ID | 19563310 |
|---|---|
| Authors | Kaj Syrjänen (0000-0002-9795-7284, Tampere University, corresponding author), Terhi Honkola (0000-0002-3330-0857, University of Turku, corresponding author), Jyri Lehtinen (0000-0001-7332-1911, University of Helsinki, corresponding author), Antti Leino (0000-0003-3917-0026, Tampere University, corresponding author), Outi Vesakoski (0000-0002-7220-3347, University of Turku, corresponding author) |
| Year | 2016 |
| Volume | 6 |
| Issue | 2 |
| Pages | 235-283 |
| Publication date | 2016-01-01 |
| Peer Reviewed | Yes |
| Open Access | No |
| Type | ARTICLE |
| Venue | Language Dynamics and Change (JOURNAL) |
| Journal identifiers | ISSN: 2210-5824 • E-ISSN: 2210-5832 |
| Publisher | Walter de Gruyter GmbH (PUBLISHER • DE) |
| DOI | 10.1163/22105832-00602002 |
| OpenAlex | W2567416017 |
| Language | EN |
| Citations received | 7 |
| References cited | 5 |
The adoption of evolutionary approaches to study language change as a type of non-biological evolution has gained increasing interest and introduced a variety of quantitative tools to linguistics. The focus has thus far mainly been on language families, or ‘linguistic macroevolution,’ and taken the shape of linguistic phylogenetics. Here we explore whether evolutionary methods could be applicable for studying intra-lingual variation (‘linguistic microevolution’) by testing a population genetic clustering method for analyzing the ‘population structure’ of Finnish dialects. We compare the results with traditional dialect divisions established in the literature and with K -medoids clustering, which is free from biological assumptions. The results are encouragingly similar to each other and agree with traditional views, suggesting that population genetic tools could be a useful addition to the dialectological toolkit. We also show how the results of the model-based clustering could serve as a basis for further study
Biology · Cluster analysis · Evolutionary biology · Linguistics · Macroevolution · Microevolution · Phylogenetic tree · Population · Sociology · Computer Science · Demography · Language and cultural evolution · Linguistic Variation and Morphology · Natural Language Processing Techniques · Artificial Intelligence
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| Unique citing works | 7 |
|---|---|
| Citations per year | 1 |
| Citation span | 2019 - 2025 (7) |
| Citation velocity | recent |
| Highly cited | No |
| Citation types | Neutral: 6 |