信息密集环境中情报研究工作的内容与方向

数据密集已成为信息社会的主要特征之一。本文分析了情报研究工作在信息密集环境中所面临的挑战,提出了信息密集环境中情报研究工作组织方式、研究内容、产品形式和服务模式,探讨了适应信息密集环境的情报工作的发展方向。     

巧用Word创建自定义特殊符号栏

MS Word已成为科技期刊编辑工作中的主要应用工具,编辑文稿时经常要输入本专业领域的特殊符号,单纯的依靠Word的插入→符号,烦琐而无序.为此本文在前人研究的基础上总结了两种可行性很强的特殊符号栏的自定义方法,帮助科技期刊编辑提高工作效率.     

主成分分析在人事考核中的应用

单位定期对所属员工进行人事考核是掌握人员动态、促进工作积极性和注重人才培养的重要措施之一。考核的结果应能真实全面的反映出每一个员工目前的工作业绩、工作态度和工作能力等方面,并能配合人才储备和培养计划提出合理方案,为人事升迁调配、奖惩褒贬提供合理依据。人事考核是多方面综合的考核,考核中不同类型的人各因素得分之间会出现高低交错的现象,使考核者很难直观的进行准确评定。为解决这方面问题,对人事考核的资料采用主成分分析的方法进行处理。     

Chinese Borrowings in English

There are eight types of English word formation, which are widely used nowadays in English. Among them, Borrowings, as one of widely used types of English word formation, has drawn people's attention b...     

The role of strategy-based motivation in mathematical problem solving: The case of learner-generated drawings

This study investigated the role of strategy-based motivation (SBM) in solving real-world geometry problems. Students from 19 classes (N = 437) were assigned to the strategy training or control condition. Before the treatment, students were asked about their SBM (i.e., self-efficacy, cost, and value). After the treatment, they were instructed to generate drawings and solve problems. The results revealed that self-efficacy expectations and cost (but not value) regarding the drawing strategy predicted drawing quality and performance. The relation between SBM and performance was mediated by drawing quality. SBM did not moderate the effects of strategy training on drawing quality or performance. The results emphasize the importance of SBM in the context of strategy training for predicting the quality of strategy use and problem-solving performance. The results are consistent with assumptions of expectancy-value and learner-generated drawing theories. One practical implication is that attention should be given to SBM in mathematics lessons.     

康德先验对象之初探

与在康德哲学中一直引起人们关注的物自体概念一样,先验对象也是一个在哲学界极有争议的概念.其真实的所指在康德的《纯粹理性批判》中不曾明确,其所指称的对象也在不同的哲学家那里有着不同的含义.它是康德哲学不可跨过的重要概念.     

Pathways to word problem solving: The mediating roles of schema construction and mathematical vocabulary

When solving word problems, many children encounter difficulties in making sense of the information and integrate it into a meaningful schema. This is the fundamental phase on which subsequent problem solution depends. To better understand the processing underlying this fundamental phase, this study examined the roles of schema construction and knowledge of mathematical vocabularies in word problem solving. The participants were 139 Chinese third graders studying in Hong Kong. Path analysis showed that there were two kinds of pathways to word problem solving: language-related and number-related. In particular, reading fluency was related to word problem solving in two mediated language-related pathways: one via schema construction, the other via knowledge of mathematical vocabularies. In the number-related pathway, arithmetic concept was related to word problem solving via knowledge of mathematical vocabularies. These findings highlight the specific roles of schema construction and mathematical vocabulary in word problem solving, thereby providing useful implications of how best to support children in understanding and integrating the information from the problem.     

A Prospect-Guided global query expansion strategy using word embeddings

The effectiveness of query expansion methods depends essentially on identifying good candidates, or prospects, semantically related to query terms. Word embeddings have been used recently in an attempt to address this problem. Nevertheless query disambiguation is still necessary as the semantic relatedness of each word in the corpus is modeled, but choosing the right terms for expansion from the standpoint of the un-modeled query semantics remains an open issue. In this paper we propose a novel query expansion method using word embeddings that models the global query semantics from the standpoint of prospect vocabulary terms. The proposed method allows to explore query-vocabulary semantic closeness in such a way that new terms, semantically related to more relevant topics, are elicited and added in function of the query as a whole. The method includes candidates pooling strategies that address disambiguation issues without using exogenous resources. We tested our method with three topic sets over CLEF corpora and compared it across different Information Retrieval models and against another expansion technique using word embeddings as well. Our experiments indicate that our method achieves significant results that outperform the baselines, improving both recall and precision metrics without relevance feedback.