Hidden Division of Labor in Scientific Teams Revealed Through 1.6 Million LaTeX Files

TL;DR

This study analyzes 1.6 million LaTeX files to uncover hidden division of labor in scientific teams, revealing that authors specialize in conceptual or technical sections, challenging traditional authorship norms.

Contribution

It introduces the first large-scale dataset on author contributions derived from LaTeX macros, validating its reliability and uncovering implicit labor division in scientific collaborations.

Findings

Authors tend to specialize in conceptual or technical sections.

The dataset is validated with high precision against self-reports and norms.

Reveals implicit division of labor challenging conventional authorship practices.

Abstract

Recognition of individual contributions is fundamental to the scientific reward system, yet coauthored papers obscure who did what. Traditional proxies-author order and career stage-reinforce biases, while contribution statements remain self-reported and limited to select journals. We construct the first large-scale dataset on writing contributions by analyzing author-specific macros in LaTeX files from 1.6 million papers (1991-2023) by 2 million scientists. Validation against self-reported statements (precision = 0.87), author order patterns, field-specific norms, and Overleaf records (Spearman's rho = 0.6, p < 0.05) confirms the reliability of the created data. Using explicit section information, we reveal a hidden division of labor within scientific teams: some authors primarily contribute to conceptual sections (e.g., Introduction and Discussion), while others focus on technical sections (e.g., Methods and Experiments). These findings provide the first large-scale evidence of implicit labor division in scientific teams, challenging conventional authorship practices and informing institutional policies on credit allocation.