强化理论视角下复用科学数据增值回溯管理模型设计  

Research on the Value Appreciation Backtracking Management Model for Reusing Scientific Data from the Perspective of Reinforcement Theory

作　　者：高俊峰 梁冰[1] 屈宝强[1] Gao Junfeng;Liang Bing;Qu Baoqiang(Institute of Scientific and Technical Information of China,Beijing 100038;School of Economics and Management,Beihua University,Jilin Jilin 132013)

机构地区：[1]中国科学技术信息研究所,北京100038 [2]北华大学经济管理学院,吉林吉林132013

出　　处：《情报理论与实践》2024年第9期81-87,共7页Information Studies:Theory & Application

基　　金：中央级公益性科研院所基本科研业务项目“开源科技情报智能分析系统与应用场景建设”(项目编号:ZD2023-13);中国博士后科学基金第69批面上项目“国家科技计划项目的科学数据开放共享机制研究”(项目编号:2021M693106)的成果。

摘　　要：[目的/意义]目前,融合Altmetrics指标和引文指标越来越成为学者学术影响力评价的趋势所在,但是现有的评价体系易忽略学术潜在影响力维度且尚未足够重视学者学术传播过程中的互动关系,因此难以满足复杂信息环境下对学者进行全面、准确评价需求。[方法/过程]传播学中的德弗勒互动过程模式理论和高潜力人才评价中的A-FAST模型理论启发了对于互动视角下学者学术影响力多维评价模型的研究。基于上述理论基础,文章在专业影响力、传播影响力、社会影响力维度的基础上补充了潜在影响力维度,构建了互动视角下学者学术影响力多维评价模型,并梳理了4个维度的具体指标体系,明确其适用场景。[结果/结论]根据构建的学者学术影响力多维评价模型和指标体系,对石墨烯领域中国学者的学术影响力进行评价应用,验证评价模型的有效性和可行性。[局限]存在评价数据类型单一和模型验证情景简化等方面的不足,以待后续研究进一步完善。[Purpose/significance]The paper construct a value appreciation backtracking management model for reusing scien-tific data,in order to reflux,organize,evaluate,accumulate,and use various value appreciation achievements of reusing scien-tific data.[Method/process]Based on the perspective of reinforcement theory,we define the management elements of value ap-preciation backtracking for reusing scientific data,including reinforcement subjects,reinforcement objects,reinforcement behav-iors,reinforcer,and scientific data.By sorting out the functions and relationships of each element,explain the mechanism of value appreciation backtracking for reusing data from the perspective of reinforcement theory.Then a four layers reusing data value appreci-ation backtracking management model was constructed,and the institutional layer,rule layer,business layer,analysis application layer of the model were interpreted layer by layer.[Result/conclusion]The value appreciation backtracking mechanism and man-agement model of reusing scientific data obtained from the perspective of reinforcement theory can supplement and improve the sub-sequent management process of scientific data sharing,thereby achieving in-depth discovery and continuous release of the implicit value of scientific data.

关 键 词：学术影响力评价 Altmetrics 互动过程模式 潜在影响力 

分 类 号：G353.1[文化科学—情报学]