我国高校图书馆虚拟馆藏的开发

一、高校图书馆虚拟馆藏构成及特色根据虚拟馆藏的建设方式，高校图书馆虚拟馆藏可分为自建馆藏与引入馆藏两大类。自建馆藏包括书目数据信息库、文本数据信息库、实体馆藏的数字化资源、自建光盘磁盘数据库、网络信息资源深加工信息、自建专题数据库、高校数据库整合资源及高校图书馆的OPAC系统资源。引入馆藏则包括购买或试用的各类数据库产品(如CNKI、WAP、EI等)、网络导航资源、电子出版物(电子图书、电子期刊等)、OCLC联机检索信息及其他     

书籍装帧中的仿古设计及其审美意蕴

仿古书籍装帧设计因其与传统文化密不可分的关联而在现实中大量运用,其设计与渊源大致有几种形式:或完全以古代书籍样式和纸质类型进行装订,或借用线装面貌样式而采用现代的纸张、剪裁和字体,或采用古代书籍的元素符号和表现形态,或成为装饰性的物件.其中所追求的审美意蕴是经典书籍装帧样式所具有的恒常价值,古代书籍装帧审美的现代运用,还包括当下书籍装帧设计审美求新的需要.仿古书籍装帧的艺术形式及其形式美的法则、艺术美的规律是客体存在,内中所蕴含的独特审美意蕴是设计得到普遍认可的内涵所在.     

面向学术图书的Altmetrics指标分析

[目的/意义] 探讨Altmetrics指标对学术图书影响力进行评价的有效性,为学术图书评价工作提出合理建议。[方法/过程] 获取Twitter提及量、Mendeley阅读量、在线书评数量以及馆藏量指标数据,对数据集的覆盖率、分位数等统计量分析后,将被引频数与Altmetrics指标进行了指标间相关系数检验,再对高Altmetrics指标值的学术图书进行年份分布、学科差异及图书主题等实证分析,探究各指标在学术图书影响力评价中的应用。[结果/结论] 传统计量指标被引频数与Altmetrics指标之间的相关性较低,说明Altmetrics可以作为学术图书评价的一个新视角,不同Altmetrics指标反映了学术图书影响力的不同维度。未来的学术图书影响力评价建议结合学术图书的年份、学科等特征,将传统的引文与Altmetrics指标相结合,探索更全面有效的评价机制。     

基于LDA主题模型的学术谱系内知识传承研究——以谈家桢为核心的遗传学学术谱系为例

[目的/意义]学术谱系以知识传承的方式助推科学发展。研究知识传承特征,探索学术谱系传承模式及其学术产出影响效能,为探索人才成长规律及人才政策制定提供参考。[方法/过程]基于LDA主题模型,以遗传学领域谈家桢为核心的学术谱系成员发表的期刊文献为研究对象,对该谱系成员的研究主题进行抽取,借鉴生物学"遗传"与"变异"的概念,根据主题相似度将谱系成员划分为"遗传学者"变异学者"和"非遗传非变异学者",并对三种学者的学术绩效进行分析。[结果/结论]分析结果表明,谈家桢学术谱系内"遗传学者"和"变异学者"的学术绩效相对较高;"非遗传非变异学者"的数量占比最多,但学术绩效相对较低;"遗传学者"与"变异学者"在不同主题下的分布具有明显差异。     

How U.S. and Chinese journalists think about plagiarism

A comparison of 1,096 professional journalists in China and the United States on attitudes toward attribution and plagiarism reveals Chinese journalists were more likely to see attribution as a practice to be embraced regardless of career longevity and culture, suggesting journalistic norms are more important than a collectivist orientation. Attribution was more likely to be embraced by those who see principles as more important than expediency, affirming research that plagiarism is hardly a monolithic concept. Overall, journalists in the two nations did not vary significantly in their attitudes toward plagiarism, despite vast differences in culture and politics as well as evidence that in some other fields China is more accepting of reusing material without attribution. The data show that among journalists, attitudes toward plagiarism are shared across national boundaries, reinforcing related research showing that a journalism culture exists and is shared at least in part across national boundaries.     

智慧图书馆建设误区与建设策略

[目的/意义] 探究构建智慧图书馆的主要策略,回答什么是智慧图书馆,智慧图书馆是什么样式,建设智慧图书馆的策略,解决智慧图书馆建设中一些模糊不清的理论与实践问题,以拓宽视野。[方法/过程] 采用文献调研与实地调研相结合,梳理和分析图书馆发展历程,指出智慧图书馆建设中出现的误区,提出建设策略。[结果/结论] 提出构建智慧图书馆五大策略,即整体的目标分析与设计;从数据到大数据;多元数据的融合;协调合作与分步落地;创新环境营造。     

CrossCheck检测平台及信息核实在学术不端防治中的作用

采用CrossCheck检测平台(http://www.ithenticate.com/)对英文科技论文进行比对,通过分析查重比对结果可初步判断该论文是否涉嫌学术不端,进而判断该论文的新颖性和研究热门程度.查重比对可为编辑处理稿件提供有力的支持,为审者审稿提供必要的参考,以便给出公正的审理意见.另外,讨论了审者信息及作者信息的核实对干预学术不端行为的作用.     

基于DEMATEL模型的我国政府信息资源跨部门共享的关键影响因素研究

[目的/意义] 新时代下,跨部门的信息资源共享已经成为加快政府职能转变,提高政府服务效率和透明度的重要事业。明确目前政府信息资源共享有效实施的关键因素,无论对于政府准确把脉行动方向还是学界聚焦政府信息共享的根源性问题都有着重要的意义。[方法/过程] 通过综合集成法梳理出近年来被学界所关注的8个维度的政府信息资源共享影响因素,通过复杂系统DEMATEL模型,构建政府信息资源共享影响因素的关系矩阵。[结果/结论] 经测算,本研究发现11个影响政府信息资源共享实施的关键因素,这些关键因素主要集中在法规政策与标准维度、体制维度、机制维度和技术维度上。     

Using word embeddings in Twitter election classification

Word embeddings and convolutional neural networks (CNN) have attracted extensive attention in various classification tasks for Twitter, e.g. sentiment classification. However, the effect of the configuration used to generate the word embeddings on the classification performance has not been studied in the existing literature. In this paper, using a Twitter election classification task that aims to detect election-related tweets, we investigate the impact of the background dataset used to train the embedding models, as well as the parameters of the word embedding training process, namely the context window size, the dimensionality and the number of negative samples, on the attained classification performance. By comparing the classification results of word embedding models that have been trained using different background corpora (e.g. Wikipedia articles and Twitter microposts), we show that the background data should align with the Twitter classification dataset both in data type and time period to achieve significantly better performance compared to baselines such as SVM with TF-IDF. Moreover, by evaluating the results of word embedding models trained using various context window sizes and dimensionalities, we find that large context window and dimension sizes are preferable to improve the performance. However, the number of negative samples parameter does not significantly affect the performance of the CNN classifiers. Our experimental results also show that choosing the correct word embedding model for use with CNN leads to statistically significant improvements over various baselines such as random, SVM with TF-IDF and SVM with word embeddings. Finally, for out-of-vocabulary (OOV) words that are not available in the learned word embedding models, we show that a simple OOV strategy to randomly initialise the OOV words without any prior knowledge is sufficient to attain a good classification performance among the current OOV strategies (e.g. a random initialisation using statistics of the pre-trained word embedding models).