灰色预测法在情报研究中的应用研究综述     

张晶晶  李泽霞 《图书情报工作》2021,64(24):106-114

[目的/意义] 灰色预测法可有效处理情报研究中广泛存在的小样本数据，通过对灰色预测法在情报研究中的应用情况进行梳理，总结其在应用过程中存在的不足，为灰色预测法在情报研究中的进一步应用提供参考。[方法/过程] 通过综述情报研究中涉及灰色预测法的相关文献，从数据选取、模型构建和解决的问题等方面对情报研究中灰色预测法的应用进行概述，总结当前情报研究中灰色预测法的应用所存在的问题，并提出改进建议。[结果/结论] 在方法应用上，已有研究主要采用数列灰预测，且模型集中在单变量灰色预测模型，根据预测对象不同，灰色预测法已经在包括期刊分析、图书馆运行管理、热点主题分析及科研机构评价方面得到了很好的应用，未来可根据预测对象特点及研究目标尝试不同的灰色预测方法，扩宽灰色预测法在其他方面的情报研究问题中的应用。  相似文献   

图书馆情报学主要研究方法:了解、选择及使用     

储荷婷 《图书情报工作》2019,63(1):146-152

[目的/意义]研究方法在学术研究中发挥着至关重要的作用。确认图书馆情报学领域主要的研究方法，并对它们进行了解熟悉，以在开展研究时能合理选择、灵活使用，确保研究质量。[方法/过程]对近2 000篇图书馆情报学领域的研究文献以及相关研究方法论文进行内容分析，在此基础上对研究方法的类分命名、图书馆情报学界主要的研究方法的确定、特点和使用注意事项进行介绍和讨论。[结果/结论]研究方法应以数据收集法而不是数据分析法命名。图书馆情报学领域常用的研究方法包括实验法、问卷法、理论研讨法、内容分析法、访谈法和书目计量法，每种方法都有各自的特点。因而在选择使用时，既应根据具体研究课题及研究方法之特性，也要考虑使用注意事项，并尽量在同一研究中采用两种或更多的方法，以扬长避短，更有效地展开研究。  相似文献   

我国冬季项目跨界跨项选材研究与启示     

郝月蓉 《河北体育学院学报》2020,34(3):42-46

运用文献资料法和逻辑分析法，对冬季项目跨界跨项选材问题进行辩证分析。认为，2022年北京冬奥会、政府专项政策导向及国内外成功的跨界经验，为我国开展冬季项目跨界跨项选材工作提供了重要的机遇和支持，但跨界跨项选材任务艰巨、冬季项目人才缺口大、运动员培养时间不足、跨界跨项风险大等问题也不容忽视。为进一步做好跨界跨项选材工作，为我国冬季项目持续发展提供充足的人力支持，应积极落实国家政策，做到科学跨选；依托同项群项目选材，跨用传统优势项目人才；抓住冬奥发展机遇，尽快建立完善的人才培养体系。  相似文献   

大学经典推荐书目的遴选标准与发展特点     

张微 《图书情报工作》2020,64(7):67-72

[目的/意义] 经典阅读处于学生通识教育的核心环节，大学经典推荐书目在"读什么""怎么读"的核心教育环节发挥着重要作用。分析和确立大学经典推荐书目的遴选标准，探究其发展特点，对于优化经典书目编制、推动经典阅读，具有十分重要的意义。[方法/过程] 通过网络调研和电话访谈梳理部分"双一流"高校的官方经典推荐书目，分析我国高校经典推荐书目的产生过程与背景。[结果/结论] 大学经典推荐书目在遴选上重视中国传统经典，也注重借鉴西方经典；人文社会科学经典与自然科学经典遴选相对均衡；发掘元典书目新生，重视充实有价值的影响书目。同时，在阅读推广实践中，呈现出尊重阅读主体、重视首因效应发挥、丰富创新推介载体，以及差异化、层次化、定制化的发展特点，体现出高校党政领导的重视和图书馆的主动作为。  相似文献   

灰色预测法在情报研究中的应用研究综述     

张晶晶  李泽霞 《图书情报工作》2020,64(24):106-114

[目的/意义] 灰色预测法可有效处理情报研究中广泛存在的小样本数据，通过对灰色预测法在情报研究中的应用情况进行梳理，总结其在应用过程中存在的不足，为灰色预测法在情报研究中的进一步应用提供参考。[方法/过程] 通过综述情报研究中涉及灰色预测法的相关文献，从数据选取、模型构建和解决的问题等方面对情报研究中灰色预测法的应用进行概述，总结当前情报研究中灰色预测法的应用所存在的问题，并提出改进建议。[结果/结论] 在方法应用上，已有研究主要采用数列灰预测，且模型集中在单变量灰色预测模型，根据预测对象不同，灰色预测法已经在包括期刊分析、图书馆运行管理、热点主题分析及科研机构评价方面得到了很好的应用，未来可根据预测对象特点及研究目标尝试不同的灰色预测方法，扩宽灰色预测法在其他方面的情报研究问题中的应用。  相似文献   

Co-occurrence of adverse childhood experiences and its association with family characteristics. A latent class analysis with Dutch population data     

《Child abuse & neglect》2019

BackgroundAlthough adverse childhood experiences (ACEs) are relatively common among children, there is limited knowledge on the co-occurrence of such experiences.ObjectiveThe current study therefore investigates co-occurrence of childhood adversity in the Netherlands and whether specific clusters are more common among certain types of families.Participants and SettingRepresentative data from the Family Survey Dutch population 2018 (N = 3,128) are employed.MethodWe estimate Latent Class Analysis (LCA) models to investigate co-occurrence of ACEs. As ACEs we examine maltreatment, household dysfunction, demographic family events, as well as financial and chronic health problems. Gradual measures for maltreatment and financial problems are studied to make it possible to differentiate with regard to the severity of experiences.ResultsOur results show that four ACE clusters may be identified: ‘Low ACE’, ‘Moderate ACE: Household dysfunction’, ‘Moderate ACE: Maltreatment’ and ‘High ACE’. Regression analyses indicated that mother’s age at first childbirth and the number of siblings were related to experiencing childhood adversity. We found limited evidence for ACEs to be related to a family’s socioeconomic position.ConclusionThe found clusters of ACEs reflect severity of childhood adversity, but also the types of adversity a child experienced. For screening and prevention of childhood adversity as well as research on its consequences, it is relevant to acknowledge this co-occurrence of types and severity of adversity.  相似文献   

Finding the missing Einsteins: Expanding the breadth of cognitive and noncognitive measures used in academic services     

《Contemporary educational psychology》2020

Education researchers, policymakers, and practitioners are concerned with identifying and developing talent for students with fewer opportunities, especially students from historically marginalized groups. An emerging body of research suggests “universally screening” or testing all students, then matching those students with appropriate educational challenges, is effective in helping marginalized students. However, most tests have focused on two areas: math and verbal reasoning. We leverage three nationally representative samples of the U.S. population at different time points that include both novel cognitive measures (e.g., spatial, mechanical, and abstract reasoning) and non-cognitive measures (e.g., conscientiousness, creativity or word fluency, leadership skill, and artistic skill) to uncover which measures would improve proportional representation of marginalized groups in talent identification procedures. We find that adding spatial reasoning measures in particular—as well as other non-cognitive measures such as conscientiousness, leadership, and creativity—are worthwhile to consider for universal screening procedures for students to narrow achievement gaps at every level of education, including for gifted students. By showing that these nontraditional measures both improve proportional representation of underrepresented groups and have reasonable predictive validity, we also broaden the definition of what it means to be “gifted” and expand opportunities for students from historically marginalized groups.  相似文献   

Drivers of information technology choice by individuals     

《International Journal of Information Management》2021

Technology acceptance has spawned considerable research in technology adoption, technology use, and technology switching. However, technology choice—i.e., an individual’s selection of a technology from a set of technologies that support similar tasks—has received limited attention in information systems research. This research was aimed at identifying the drivers of technology choice through a series of activities in two universities, in which students chose an information technology tool from various alternatives to complete the given tasks. A thematic analysis was conducted on the reasons for technology choice reported by 249 students, which yielded 18 technology, user, and environmental drivers that influenced individuals’ technology choice. This study provides insights into the drivers generally applicable for technology choice and drivers applicable in specific contexts. Implications for research and practice are discussed.  相似文献   

Word sense disambiguation based on context selection using knowledge-based word similarity     

Sunjae Kwon  Dongsuk Oh  Youngjoong Ko 《Information processing & management》2021,58(4):102551

In this paper, we introduce a novel knowledge-based word-sense disambiguation (WSD) system. In particular, the main goal of our research is to find an effective way to filter out unnecessary information by using word similarity. For this, we adopt two methods in our WSD system. First, we propose a novel encoding method for word vector representation by considering the graphical semantic relationships from the lexical knowledge bases, and the word vector representation is utilized to determine the word similarity in our WSD system. Second, we present an effective method for extracting the contextual words from a text for analyzing an ambiguous word based on word similarity. The results demonstrate that the suggested methods significantly enhance the baseline WSD performance in all corpora. In particular, the performance on nouns is similar to those of the state-of-the-art knowledge-based WSD models, and the performance on verbs surpasses that of the existing knowledge-based WSD models.  相似文献   

A novel personality detection method based on high-dimensional psycholinguistic features and improved distributed Gray Wolf Optimizer for feature selection     

《Information processing & management》2023,60(2):103217

Existing personality detection methods based on user-generated text have two major limitations. First, they rely too much on pre-trained language models to ignore the sentiment information in psycholinguistic features. Secondly, they have no consensus on the psycholinguistic feature selection, resulting in the insufficient analysis of sentiment information. To tackle these issues, we propose a novel personality detection method based on high-dimensional psycholinguistic features and improved distributed Gray Wolf Optimizer (GWO) for feature selection (IDGWOFS). Specifically, we introduced the Gaussian Chaos Map-based initialization and neighbor search strategy into the original GWO to improve the performance of feature selection. To eliminate the bias generated when using mutual information to select features, we adopt symmetric uncertainty (SU) instead of mutual information as the evaluation for correlation and redundancy to construct the fitness function, which can balance the correlation between features–labels and the redundancy between features–features. Finally, we improve the common Spark-based parallelization design of GWO by parallelizing only the fitness computation steps to improve the efficiency of IDGWOFS. The experiments indicate that our proposed method obtains average accuracy improvements of 3.81% and 2.19%, and average F1 improvements of 5.17% and 5.8% on Essays and Kaggle MBTI dataset, respectively. Furthermore, IDGWOFS has good convergence and scalability.  相似文献