Writing Patterns Reveal a Hidden Division of Labor in Scientific Teams
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866908326671941632 |
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| author | Yang, Lulin Pei, Jiaxin Wu, Lingfei |
| author_facet | Yang, Lulin Pei, Jiaxin Wu, Lingfei |
| contents | The recognition of individual contributions is central to the scientific reward system, yet coauthored papers often obscure who did what. Traditional proxies like author order assume a simplistic decline in contribution, while emerging practices such as self-reported roles are biased and limited in scope. We introduce a large-scale, behavior-based approach to identifying individual contributions in scientific papers. Using author-specific LaTeX macros as writing signatures, we analyze over 730,000 arXiv papers (1991-2023), covering over half a million scientists. Validated against self-reports, author order, disciplinary norms, and Overleaf records, our method reliably infers author-level writing activity. Section-level traces reveal a hidden division of labor: first authors focus on technical sections (e.g., Methods, Results), while last authors primarily contribute to conceptual sections (e.g., Introduction, Discussion). Our findings offer empirical evidence of labor specialization at scale and new tools to improve credit allocation in collaborative research. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2504_14093 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Writing Patterns Reveal a Hidden Division of Labor in Scientific Teams Yang, Lulin Pei, Jiaxin Wu, Lingfei Social and Information Networks The recognition of individual contributions is central to the scientific reward system, yet coauthored papers often obscure who did what. Traditional proxies like author order assume a simplistic decline in contribution, while emerging practices such as self-reported roles are biased and limited in scope. We introduce a large-scale, behavior-based approach to identifying individual contributions in scientific papers. Using author-specific LaTeX macros as writing signatures, we analyze over 730,000 arXiv papers (1991-2023), covering over half a million scientists. Validated against self-reports, author order, disciplinary norms, and Overleaf records, our method reliably infers author-level writing activity. Section-level traces reveal a hidden division of labor: first authors focus on technical sections (e.g., Methods, Results), while last authors primarily contribute to conceptual sections (e.g., Introduction, Discussion). Our findings offer empirical evidence of labor specialization at scale and new tools to improve credit allocation in collaborative research. |
| title | Writing Patterns Reveal a Hidden Division of Labor in Scientific Teams |
| topic | Social and Information Networks |
| url | https://arxiv.org/abs/2504.14093 |