科学是知识景观中的探索:追踪热点还是寻找机会?

IF 3 2区 计算机科学 Q1 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS
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引用次数: 0

摘要

摘要 科学家对研究课题的选择可以被看作是有认知局限的个体在受个人和社会因素影响的复杂认知环境中进行的探索过程。虽然现有的理论研究提供了有价值的见解,但现代科学错综复杂的多面性阻碍了实证实验的实施。本研究利用先进的地理信息系统(GIS)技术来研究科学家之间话题转换的模式和动态机制。通过构建 6 个大型学科的知识空间,我们描绘了科学家在这一空间内的话题转换轨迹,测量了研究区域在不同子空间内的流动和距离。我们的研究结果表明,在个人层面上,主题转换的模式以保守为主,科学家主要探索本地知识空间。此外,模拟建模分析发现,由特定区域内科学家的集中程度所驱动的研究强度是课题转换的主要促进因素。相反,领域之间的知识距离则是探索的一大障碍。值得注意的是,尽管在子领域的交叉点有可能出现突破性发现,但经验证据表明,这些机会并没有对科学家产生强大的吸引力,导致他们倾向于熟悉的研究领域。我们的研究为科学知识生产的探索动力提供了宝贵的见解,突出了个人认知、社会因素和知识景观本身内在结构的影响。这些发现为理解并可能塑造科学进步的进程提供了一个框架。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Science as exploration in a knowledge landscape: tracing hotspots or seeking opportunity?

Abstract

The selection of research topics by scientists can be viewed as an exploration process conducted by individuals with cognitive limitations traversing a complex cognitive landscape influenced by both individual and social factors. While existing theoretical investigations have provided valuable insights, the intricate and multifaceted nature of modern science hinders the implementation of empirical experiments. This study leverages advancements in Geographic Information System (GIS) techniques to investigate the patterns and dynamic mechanisms of topic-transition among scientists. By constructing the knowledge space across 6 large-scale disciplines, we depict the trajectories of scientists’ topic transitions within this space, measuring the flow and distance of research regions across different sub-spaces. Our findings reveal a predominantly conservative pattern of topic transition at the individual level, with scientists primarily exploring local knowledge spaces. Furthermore, simulation modeling analysis identifies research intensity, driven by the concentration of scientists within a specific region, as the key facilitator of topic transition. Conversely, the knowledge distance between fields serves as a significant barrier to exploration. Notably, despite potential opportunities for breakthrough discoveries at the intersection of subfields, empirical evidence suggests that these opportunities do not exert a strong pull on scientists, leading them to favor familiar research areas. Our study provides valuable insights into the exploration dynamics of scientific knowledge production, highlighting the influence of individual cognition, social factors, and the intrinsic structure of the knowledge landscape itself. These findings offer a framework for understanding and potentially shaping the course of scientific progress.

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来源期刊
EPJ Data Science
EPJ Data Science MATHEMATICS, INTERDISCIPLINARY APPLICATIONS -
CiteScore
6.10
自引率
5.60%
发文量
53
审稿时长
13 weeks
期刊介绍: EPJ Data Science covers a broad range of research areas and applications and particularly encourages contributions from techno-socio-economic systems, where it comprises those research lines that now regard the digital “tracks” of human beings as first-order objects for scientific investigation. Topics include, but are not limited to, human behavior, social interaction (including animal societies), economic and financial systems, management and business networks, socio-technical infrastructure, health and environmental systems, the science of science, as well as general risk and crisis scenario forecasting up to and including policy advice.
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