Argue, observe, assess: Measuring disciplinary identities and differences through socio-epistemic discourse
Argue, observe, assess: Measuring disciplinary identities and differences through socio-epistemic discourse
复制标题
DOI：
10.1002/asi.23271
复制
发表时间：
2015-07-01
影响因子：
3.5
通讯作者：
Sugimoto, Cassidy R.
中科院分区：
文献类型：
--
作者：
Demarest, Bradford;Sugimoto, Cassidy R.
面对大规模的复杂问题(包括气候变化、经济不平等和教育等)，跨学科合作的呼声越来越普遍；然而，由于跨学科研究中的所谓翻译问题等原因，这种合作的结果好坏参半。本文提出了一种潜在的解决方案：在一个比较哲学、心理学和物理学论文的案例研究中，通过使用社会和认知术语(我们称之为话语认识论)，定量衡量学科语言之间差异的程度和性质。使用机器学习的支持向量模型根据社会术语和认知术语的相对频率对学科进行分类，我们能够显著提高随机选择基线的准确性(区分学科的准确率高达90%)，并根据每个学科的相对存在或不存在获得最具指示性的术语集。这些清单随后根据学科的社会学和认识论研究结果进行了审议，发现它们验证了该方法对社会和认识论学科特性和对比的衡量标准。基于我们的研究结果，我们的结论是考虑到这一领域研究的受益者，包括文献计量学家、学生和科学政策制定者等，并制定了一个研究计划，扩大学科数量，考虑社会认识论随时间的变化，并将这些方法应用于非学术认识论社区(例如，政治团体)。
Calls for interdisciplinary collaboration have become increasingly common in the face of large-scale complex problems (including climate change, economic inequality, and education, among others); however, outcomes of such collaborations have been mixed, due, among other things, to the so-called translation problem in interdisciplinary research. This article presents a potential solution: an empirical approach to quantitatively measure both the degree and nature of differences among disciplinary tongues through the social and epistemic terms used (a research area we refer to as discourse epistemetrics), in a case study comparing dissertations in philosophy, psychology, and physics. Using a support-vector model of machine learning to classify disciplines based on relative frequencies of social and epistemic terms, we were able to markedly improve accuracy over a random selection baseline (distinguishing between disciplines with as high as 90% accuracy) as well as acquire sets of most indicative terms for each discipline by their relative presence or absence. These lists were then considered in light of findings of sociological and epistemological studies of disciplines and found to validate the approach's measure of social and epistemic disciplinary identities and contrasts. Based on the findings of our study, we conclude by considering the beneficiaries of research in this area, including bibliometricians, students, and science policy makers, among others, as well as laying out a research program that expands the number of disciplines, considers shifts in socio-epistemic identities over time and applies these methods to nonacademic epistemological communities (e.g., political groups).