Disagreement is essential to scientific progress. However, the extent of disagreement in science, its evolution over time, and the fields in which it happens, remains largely unknown. Leveraging a massive collection of scientific texts, we develop a cue-phrase based approach to identify instances of disagreement citations across more than four million scientific articles. Using this method, we construct an indicator of disagreement across scientific fields over the 2000-2015 period. In contrast with black-box text classification methods, our framework is transparent and easily interpretable. We reveal a disciplinary spectrum of disagreement, with higher disagreement in the social sciences and lower disagreement in physics and mathematics. However, detailed disciplinary analysis demonstrates heterogeneity across sub-fields, revealing the importance of local disciplinary cultures and epistemic characteristics of disagreement. Paper-level analysis reveals notable episodes of disagreement in science, and illustrates how methodological artefacts can confound analyses of scientific texts. These findings contribute to a broader understanding of disagreement and establish a foundation for future research to understanding key processes underlying scientific progress.

中文翻译：

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测量科学中的分歧

分歧对科学进步至关重要。然而，科学中分歧的程度、它随时间的演变以及它发生的领域在很大程度上仍然未知。利用大量科学文本，我们开发了一种基于提示短语的方法来识别超过 400 万篇科学文章中的不同引用实例。使用这种方法，我们构建了 2000 年至 2015 年期间跨科学领域分歧的指标。与黑盒文本分类方法相比，我们的框架透明且易于解释。我们揭示了一系列学科的分歧，社会科学的分歧较大，物理和数学的分歧较小。然而，详细的学科分析显示了子领域之间的异质性，揭示地方学科文化的重要性和分歧的认知特征。论文层面的分析揭示了科学中值得注意的分歧，并说明了方法论的人工制品如何混淆科学文本的分析。这些发现有助于更广泛地理解分歧，并为未来的研究奠定基础，以了解科学进步背后的关键过程。