Purpose:Interdisciplinary research has become a critical approach to addressing complex societal,economic,technological,and environmental challenges,driving innovation and integrating scientific knowledge.While interd...Purpose:Interdisciplinary research has become a critical approach to addressing complex societal,economic,technological,and environmental challenges,driving innovation and integrating scientific knowledge.While interdisciplinarity indicators are widely used to evaluate research performance,the impact of classification granularity on these assessments remains underexplored.Design/methodology/approach:This study investigates how different levels of classification granularity-macro,meso,and micro-affect the evaluation of interdisciplinarity in research institutes.Using a dataset of 262 institutes from four major German non-university organizations(FHG,HGF,MPG,WGL)from 2018 to 2022,we examine inconsistencies in interdisciplinarity across levels,analyze ranking changes,and explore the influence of institutional fields and research focus(applied vs.basic).Findings:Our findings reveal significant inconsistencies in interdisciplinarity across classification levels,with rankings varying substantially.Notably,the Fraunhofer Society(FHG),which performs well at the macro level,experiences significant ranking declines at meso and micro levels.Normalizing interdisciplinarity by research field confirmed that these declines persist.The research focus of institutes,whether applied,basic,or mixed,does not significantly explain the observed ranking dynamics.Research limitations:This study has only considered the publication-based dimension of institutional interdisciplinarity and has not explored other aspects.Practical implications:The findings provide insights for policymakers,research managers,and scholars to better interpret interdisciplinarity metrics and support interdisciplinary research effectively.Originality/value:This study underscores the critical role of classification granularity in interdisciplinarity assessment and emphasizes the need for standardized approaches to ensure robust and fair evaluations.展开更多
论文作为科研成果的重要表现形式,在“破五唯”的大背景下,科学遴选高水平论文至关重要,是保障代表作评价工作落地的基本抓手。引用评论是学术共同体对论文价值最直接的认可,亦是学术评价的实质性关键证据,对其内容进行深入挖掘有助于...论文作为科研成果的重要表现形式,在“破五唯”的大背景下,科学遴选高水平论文至关重要,是保障代表作评价工作落地的基本抓手。引用评论是学术共同体对论文价值最直接的认可,亦是学术评价的实质性关键证据,对其内容进行深入挖掘有助于更科学地发现高水平论文。首先,本文基于学术认同理论和证据特征阐释了模型构建的基本思想;其次,对基于内容语义的引用情感和引用功能进行重新界定和分类,并充分考虑“施引作者可信度”,构建高水平论文遴选综合加权模型;再其次,选取化学领域顶级刊物Angewandte Chemie-International Edition中的相关论文展开实证研究,结果表明,本文提出的模型可以较精准地筛选出“非常重要论文(very important paper,VIP)”;最后,与其他主流评价指标进行比较分析,证实了本文模型具有较高的区分度和鉴别度,在论文学术水平评价中具有显著优越性。展开更多
针对信息化发展中在线试卷的组卷工作中存在的问题,诸如如何让考试的试题更好地检验学生的知识水平,怎样考察学生掌握和未掌握的知识等问题,探索提出了一种自适应的组卷方法,把学生个性化信息引入其中,采用期望的试卷难度、区分度作为...针对信息化发展中在线试卷的组卷工作中存在的问题,诸如如何让考试的试题更好地检验学生的知识水平,怎样考察学生掌握和未掌握的知识等问题,探索提出了一种自适应的组卷方法,把学生个性化信息引入其中,采用期望的试卷难度、区分度作为约束条件,将从试题库选择的试题子集的难度和区分度值与期望的难度和区分度的差作为目标函数,从而提出一种个性化信息遗传组卷算法(Personalized Information Genetic Algorithm,PI-GA)。测验结果证明,在生成试卷的时候,PI-GA算法可以有效地为学生提供个性化试卷,对比几种常见的算法,执行时间最短,并且所组成的最终试卷中包含的学生未掌握试题数量具有灵活性。展开更多
文摘Purpose:Interdisciplinary research has become a critical approach to addressing complex societal,economic,technological,and environmental challenges,driving innovation and integrating scientific knowledge.While interdisciplinarity indicators are widely used to evaluate research performance,the impact of classification granularity on these assessments remains underexplored.Design/methodology/approach:This study investigates how different levels of classification granularity-macro,meso,and micro-affect the evaluation of interdisciplinarity in research institutes.Using a dataset of 262 institutes from four major German non-university organizations(FHG,HGF,MPG,WGL)from 2018 to 2022,we examine inconsistencies in interdisciplinarity across levels,analyze ranking changes,and explore the influence of institutional fields and research focus(applied vs.basic).Findings:Our findings reveal significant inconsistencies in interdisciplinarity across classification levels,with rankings varying substantially.Notably,the Fraunhofer Society(FHG),which performs well at the macro level,experiences significant ranking declines at meso and micro levels.Normalizing interdisciplinarity by research field confirmed that these declines persist.The research focus of institutes,whether applied,basic,or mixed,does not significantly explain the observed ranking dynamics.Research limitations:This study has only considered the publication-based dimension of institutional interdisciplinarity and has not explored other aspects.Practical implications:The findings provide insights for policymakers,research managers,and scholars to better interpret interdisciplinarity metrics and support interdisciplinary research effectively.Originality/value:This study underscores the critical role of classification granularity in interdisciplinarity assessment and emphasizes the need for standardized approaches to ensure robust and fair evaluations.
文摘论文作为科研成果的重要表现形式,在“破五唯”的大背景下,科学遴选高水平论文至关重要,是保障代表作评价工作落地的基本抓手。引用评论是学术共同体对论文价值最直接的认可,亦是学术评价的实质性关键证据,对其内容进行深入挖掘有助于更科学地发现高水平论文。首先,本文基于学术认同理论和证据特征阐释了模型构建的基本思想;其次,对基于内容语义的引用情感和引用功能进行重新界定和分类,并充分考虑“施引作者可信度”,构建高水平论文遴选综合加权模型;再其次,选取化学领域顶级刊物Angewandte Chemie-International Edition中的相关论文展开实证研究,结果表明,本文提出的模型可以较精准地筛选出“非常重要论文(very important paper,VIP)”;最后,与其他主流评价指标进行比较分析,证实了本文模型具有较高的区分度和鉴别度,在论文学术水平评价中具有显著优越性。
文摘针对信息化发展中在线试卷的组卷工作中存在的问题,诸如如何让考试的试题更好地检验学生的知识水平,怎样考察学生掌握和未掌握的知识等问题,探索提出了一种自适应的组卷方法,把学生个性化信息引入其中,采用期望的试卷难度、区分度作为约束条件,将从试题库选择的试题子集的难度和区分度值与期望的难度和区分度的差作为目标函数,从而提出一种个性化信息遗传组卷算法(Personalized Information Genetic Algorithm,PI-GA)。测验结果证明,在生成试卷的时候,PI-GA算法可以有效地为学生提供个性化试卷,对比几种常见的算法,执行时间最短,并且所组成的最终试卷中包含的学生未掌握试题数量具有灵活性。
