Big Team Science for language science: opportunities and challenges

Abstract The present paper advocates for incorporating Big Team Science practices into the language sciences. Big Team Science is a promising mode of collaborative research in which top-down coordination directs efforts by a large number of researchers from different institutions towards a common scientific goal. The redistribution of resources and division of labor enabled by Big Team Science may enable researchers to increase methodological rigor, systematically explore claims across a wider sample of languages, and to diversify both the languages studied and the researchers engaged. However, implementing Big Team Science practices is not without challenges, some of which are unique to the language sciences. We suggest strategies and best practices for adapting to these challenges, including theoretical and methodological pluralism, variation in conditions among data collection sites, and navigating a trade-off between maximizing methodological rigor and ability to engage with the most inclusive, typologically broad language samples.

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Publication Details

Journal
Linguistics
Published
2026-11-06
DOI
https://doi.org/10.1515/ling-2024-0233
Primary Topic
Interdisciplinary Research and Collaboration
Type
article
Field-Weighted Citation Impact
0.00

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article

Big Team Science for language science: opportunities and challenges

Tiphaine Caudrelier, Erin Michelle Buchanan, Adriana Hanulíková, Jennifer Nycz et al.
2 links
Linguistics
Interdisciplinary Research and Collaboration
article

Big Team Science for language science: opportunities and challenges

Tiphaine Caudrelier, Erin Michelle Buchanan, Adriana Hanulíková, Jennifer Nycz, Kamil Kaźmierski, Alexandra Vella, Joseph V. Casillas, Indranil Dutta, Miki Mori, Šárka Kadavá, Leticia Quesada Vázquez, Brygida Sawicka-Stępińska, Aleksandra Ćwiek, Pius W. Akumbu, Janne Lorenzen, Marie Dokovova, Rowena Garcia, Sam Hellmuth, Amalia Arvaniti, Martine Grice, Reenu Punnoose, Cong Zhang, Paul Foulkes, Onur Özsoy, Dorotea Bevivino, Ander Egurtzegi, Malte Belz, Matthew Faytak, Amanda Cardoso, Teja Rebernik, Timo Benjamin Roettger, Mark Amengual, Marzena Żygis, Jane E. Setter, Chenzi Xu, Zed Sevcikova Sehyr, Malin Spaniol, Iñigo Urrestarazu-Porta, Xiang Li, Hadley Forst
article en

Abstract

Abstract The present paper advocates for incorporating Big Team Science practices into the language sciences. Big Team Science is a promising mode of collaborative research in which top-down coordination directs efforts by a large number of researchers from different institutions towards a common scientific goal. The redistribution of resources and division of labor enabled by Big Team Science may enable researchers to increase methodological rigor, systematically explore claims across a wider sample of languages, and to diversify both the languages studied and the researchers engaged. However, implementing Big Team Science practices is not without challenges, some of which are unique to the language sciences. We suggest strategies and best practices for adapting to these challenges, including theoretical and methodological pluralism, variation in conditions among data collection sites, and navigating a trade-off between maximizing methodological rigor and ability to engage with the most inclusive, typologically broad language samples.

Linguistics
Rutgers, The State University of New Jersey (US), Centre National de la Recherche Scientifique (FR), Vrije Universiteit Brussel (BE), University of Arizona (US), University of British Columbia (CA), Université de Pau et des Pays de l'Adour (FR), Radboud University Nijmegen (NL), Harrisburg University of Science and Technology (US), University of Freiburg (DE), University of the Basque Country (ES), Jadavpur University (IN), Nanyang Technological University (SG), University of Cologne (DE), University of Strathclyde (GB), Université Sorbonne Nouvelle (FR), Georgetown University (US), University of Oslo (NO), Chapman University (US), University of California, Santa Cruz (US), Université Lumière Lyon 2 (FR), Johannes Gutenberg University Mainz (DE), University of Malta (MT), Université Paris Cité (FR), Heidelberg University (DE), University of Reunion Island (RE), Nicolaus Copernicus University (PL), Universidad Nebrija (ES), Humboldt-Universität zu Berlin (DE), University of Oxford (GB), Laboratoire d’Étude des Mécanismes Cognitifs (FR), Laboratoire de Phonétique et Phonologie (FR), Indian Institute of Technology Palakkad (IN), Laboratoire de Linguistique Formelle (FR), Langage, langues et cultures d'Afrique (FR), Centre de Recherche sur la Langue et les Textes Basques (FR), Leibniz-Centre General Linguistics (DE), University Hospital Cologne (DE), Nanfang College Guangzhou (CN), University of York (GB), University of the Philippines Manila (PH), Adam Mickiewicz University in Poznań (PL), University at Buffalo, State University of New York (US), University of Reading (GB), Newcastle University (GB)
Leverhulme Trust, Deutsche Forschungsgemeinschaft, Agence Nationale de la Recherche, Norges Forskningsråd, European Research Council
Openalex Percentile: Top 38%
Interdisciplinary Research and Collaboration
DOIOpen Access PDFOpenAlex
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