Evaluating interdisciplinary research: Disparate outcomes for topic and knowledge base

• 成果类型: Article
• 署名作者: Xiang, Sidney; Romero, Daniel M.; Teplitskiy, Misha
• 署名单位: University of Michigan System; University of Michigan; University of Michigan System; University of Michigan; University of Michigan System; University of Michigan
• 刊物名称: PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
• ISSN/ISSBN: 0027-10132
• DOI: 10.1073/pnas.2409752122
• 发表日期: 2025-04-22
• 关键词: innovation; memberships; CATEGORIES; diversity

摘要

Interdisciplinary research is essential for addressing complex global challenges, but there are concerns that scientific institutions like journals select against it. Prior work has focused largely on how interdisciplinarity relates to outcomes for published papers, but which papers get accepted for publication in the first place is unclear. Furthermore, journals may evaluate two key dimensions of interdisciplinarity,-topic and knowledge base,-differently. Topic interdisciplinarity (measured through title and abstract) may incur evaluation penalties by cutting across disciplinary evaluation standards and threatening symbolic boundaries, while knowledge-base interdisciplinarity (measured through references) may incur benefits by combining a large pool of nonredundant information. Evaluations may also depend on how well these dimensions align with each other and the intended audience. We test these arguments using data on 128,950 submissions to 62 journals across STEM disciplines, including both accepted and rejected manuscripts. We find that a 1SD increase in knowledge base interdisciplinarity is associated with a 0.9 percentage-point higher acceptance probability, while a 1SD increase in topic interdisciplinarity corresponds to a percentage-point lower acceptance probability. However, the penalty for high topicinterdisciplinarity diminishes when knowledge-base interdisciplinarity is also high, and when submitted to journals designated as interdisciplinary. These findings challenge the narrative of a uniform bias against interdisciplinary research and highlight the importance of distinguishing between its dimensions, as well as their alignment with each other and the intended audience.