Evidence map›Paper›PMID 38626198›Full record

ArticlePloS one2024

Linguistic correlates of societal variation: A quantitative analysis.

Sihan Chen, David Gil, Sergey Gaponov, Jana Reifegerste, Tessa Yuditha, Tatiana Tatarinova, Ljiljana Progovac, Antonio Benítez-Burraco

Abstract read
In one paragraph

Article in PloS one, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

7 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Sihan ChenDepartment of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, United States of America.ORCID 0000-0002-9307-5014
David GilDepartment of Linguistic and Cultural Evolution, Max Planck Institute for Evolutionary Anthropology, Leipzig, Germany.
Sergey GaponovDepartment of Biology and Computational Biology, University of LaVerne, LaVerne, CA, United States of America.
Jana ReifegersteDepartment of Neurology, Georgetown University, Washington, DC, United States of America.ORCID 0000-0002-0618-5881
Tessa YudithaDepartment of Spanish, Linguistics & Theory of Literature, University of Seville, Seville, Spain.
Tatiana TatarinovaDepartment of Biology and Computational Biology, University of LaVerne, LaVerne, CA, United States of America.
Ljiljana ProgovacLinguistics Program, Wayne State University, Detroit, MI, United States of America.
Antonio Benítez-BurracoDepartment of Spanish, Linguistics & Theory of Literature, University of Seville, Seville, Spain.ORCID 0000-0003-4574-5666

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Traditionally, many researchers have supported a uniformitarian view whereby all languages are of roughly equal complexity, facilitated by internal trade-offs between complexity at different levels, such as morphology and syntax. The extent to which the speakers' societies influence the trade-offs has not been well studied. In this paper, we focus on morphology and syntax, and report significant correlations between specific linguistic and societal features, in particular those relating to exoteric (open) vs. esoteric (close-knit) society types, characterizable in terms of population size, mobility, communication across distances, etc. We conduct an exhaustive quantitative analysis drawing upon WALS, D-Place, Ethnologue and Glottolog, finding some support for our hypothesis that languages spoken by exoteric societies tend towards more complex syntaxes, while languages spoken by esoteric societies tend towards more complex morphologies.

Indexed as

LanguageLinguisticsCommunicationHumansResearch Personnel

Identifiers

PMID38626198
PMCPMC11020942

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.