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Content, Contribution, and Knowledge Consumption: Uncovering Hidden Topic Structure and Rhetorical Signals in Scientific Texts

机译:内容,贡献和知识消耗:在科学文本中揭示隐藏的主题结构和修辞信号

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Knowledge production and scientific discourse are observable in published scholarly texts. Citations capture knowledge consumption and impact. Drawing from the sociology of science, our theoretical framework posits scientific communities as thought collectives with distinctive thought styles that embed a hidden topic structure and rhetorical signals into a journal's published articles. We hypothesize and uncover how an article's topic attributes (structure, focus, and newness) and rhetorical attributes (inclusiveness, exclusiveness, tentativeness, and certainty) are related to future knowledge consumption. We empirically test our ideas by applying text mining algorithms to model topics and extract rhetorical signals from 1,646 strategy articles composed of nearly 18 million words generating 172,237 citations over 35 years. We find that strategy articles' hidden topic structure explains 14% of variance in scientific impact. We also show that topic focus and topic newness each independently, directly, and significantly increase impact. As for newness, the first two articles published on a new topic each generate a citation premium 100%, which is higher within the focal thought collective than outside. Importantly, we find that the citation premium of newness increases with greater topic focus (which attracts attention) and greater inflow of prior intracollective knowledge (which enhances absorption). Impact also increases when authors present new topics using a rhetorical style that is more tentative than certain. Overall, our findings demonstrate that topic and rhetorical attributes, as constitutive elements of scientific content, are independently and interdependently related to the consumption of strategy articles across thought collectives in management research.
机译:知识生产和科学话语是可观察到的出版学术文本。引文捕获知识消耗和影响。从科学社会学中绘制,我们的理论框架将科学社区视为思想集体,具有独特的思想风格,旨在将隐藏的主题结构和修辞信号嵌入到日记的已发表的文章中。我们假设和揭示文章的主题属性(结构,焦点和新性)和修辞属性(包裹性,排他性,追求性和确定性)与未来知识消费有关。我们通过将文本挖掘算法应用于模型主题和提取由近1800万字的1,646个策略文章提取修辞信号,从而统一地测试我们的想法。我们发现战略文章的隐藏主题结构解释了科学影响的14%的差异。我们还显示主题焦点和主题新性,每一个独立,直接和显着增加影响。至于新的新性,在一个新的主题上发表的前两个文章,每个文章都会产生引文溢价> 100%,这在焦点思想中比外部更高。重要的是,我们发现新性的引文溢价随着更大主题的重点(引起注意)和更大的耕作者知识(增强吸收)的流入。当作者使用比某些更初步的修辞风格提出新主题时,影响也会增加。总体而言,我们的调查结果表明,主题和修辞属性,作为科学内容的本体要素,与管理研究中思想集体的战略文章的消费无关和相互依存。

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