摘要
[目的/意义]及时掌握全球OA科技期刊APC发展态势与异常情况,有利于避免国内科研经费流失,辅助决策,为国内确定面向开放获取和开放科学的发展路径提供参考.[方法/过程]使用最小二乘法拟合OA期刊的APC单价和影响力指数之间的函数关系,并设置95%的置信区间以识别溢价期刊、非溢价期刊,基于此原理建立全球OA科技期刊APC监测与异常预警模型.[结果/结论]该模型包括自动采集、监测分析、异常预警模块,以中国科学院某物理学领域研究所发文的OA期刊数据代入模型计算,发现溢价期刊占比43.5%,有4.46%的异常APC需要预警,但90%的论文发表在了性价比较高的非溢价期刊上,并且非溢价期刊均不在Beall's List名单中,溢价期刊分类符合GoOA和DOAJ的收录情况,验证了模型的有效性和可靠性.
[Purpose/significance]Grasping the development and abnormal situation of global OA Sci-Techjournals’APC in time is helpful to avoid the loss of domestic research funding,assist decision-making,and providea reference for China to determine the development path for open access and open science.[Method/process]Thispaper used the least square method to fit the functional relationship between the APC unit price and influence indexof OA journals,and set a 95%confidence interval to identify premium jourmals and non-premium journals.Based onthis principle,a global 0A Sci-Tech jourmals’APC monitoring and anomaly early waning model was established.[Result/conclusion]The model includes automatic acquisition,monitoring analysis,and anomaly early warningmodules.Take OA journals data published by an institute of physics in the Chinese Academy of Sciences as an exam-ple,it was found that premium journals accounted for 43.5%,and 4.46%of abnormal APCs required early wam-ing.However,90%of the papers were published in non-premium journals with high cost performance,and non-pre-mium journals were not in the Beall's List,premium journal classification conforms to the inclusion of GoOA andDOAJ,which verified the validity and reliability of the model.
作者
芮啸
赵展一
王昉
陈雪飞
黄金霞
Rui Xiao;Zhao Zhanyi;Wang Fang;Chen Xuefei;Huang Jinxia(National Science Library,Chinese Academy of Sciences,Beijing 100190;School of Economics and Management,University of Chinese Academy of Sciences,Beijing 100190)
出处
《图书情报工作》
CSSCI
北大核心
2021年第8期42-50,共9页
Library and Information Service
基金
国家社会科学基金项目“全球OA科技期刊出版大数据监测模型研究”(项目编号:18BTQ059)研究成果之一。
关键词
OA期刊
论文处理费用
溢价期刊
异常预警模型
开放科学
Open Access journals
article processing charges
premium jourmals
abnormal early warning model
open science
