研究发现，作者背景影响因果表述，揭示科学沟通中的社会文化因素。
Causal Language in Observational Studies: Sociocultural Backgrounds and Team Composition
- 分析9万+观察性研究摘要，用语言学方法识别因果表述模式。
- 新手作者、小团队、男性通讯作者及高不确定性规避国家更常使用因果语言。
- 提醒审稿人关注作者背景对科学表述的影响，适合科研伦理与社会学研究者。
观察性研究中因果语言的使用引发了科学传播过度夸大问题的关注。尽管有人认为因果表述应仅限于随机对照试验，但也有观点认为严谨的因果推断方法可支持观察研究中的因果主张。理想情况下，因果语言应与证据强度相匹配。然而，通过对超过9万篇观察性研究摘要进行计算语言学与回归分析，我们发现因果语言在经验较少的作者、较小的研究团队、男性通讯作者以及不确定性规避指数较高的国家中更为常见。研究结果表明，因果语言的使用不仅取决于证据强度，还受作者的社会文化背景和团队构成影响。这项工作为理解科学沟通中的系统性差异提供了新视角，并强调在评估科学主张时需关注这些人为因素。
原文摘要 · Abstract (English)
The use of causal language in observational studies has raised concerns about overstatement in scientific communication. While some argue that such language should be reserved for randomized controlled trials, others contend that rigorous causal inference methods can justify causal claims in observational research. Ideally, causal language should align with the strength of the underlying evidence. However, through the analysis of over 90,000 abstracts from observational studies using computational linguistic and regression methods, we found that causal language are more common in work by less experienced authors, smaller research teams, male last authors, and researchers from countries with higher uncertainty avoidance indices. Our findings suggest that the use of causal language is not solely driven by the strength of evidence, but also by the sociocultural backgrounds of authors and their team composition. This work provides a new perspective for understanding systematic variations in scientific communication and emphasizes the importance of recognizing these human factors when evaluating scientific claims.
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