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1.
本文运用固定效应模型,分析近十年美国公立世界一流大学的经费支出数据。研究发现:美国一流公立大学中教学科研经费是主要支出;经费总支出与一流大学的排名没有显著相关性,但部分经费支出对大学排名有显著影响。对于无医院服务支出的高校,公共服务支出与大学排名显著正相关;对于有医院服务支出的高校,辅助经营支出的增加有助于大学排名的提升,公共服务支出的影响却相反。因此,提高经费使用效率比增加总额更为重要。我国“双一流”建设高校应该革新经费支出配置理念,建立科学有效的经费配置体系,增大学校各类服务支出。  相似文献   
2.
运用文献资料法、案例分析法和数理统计法,以美国7所大学为研究对象,分别从美国这7所大学的体育学科发展现状、内涵、世界排名、学科特点进行分析。研究发现:美国这7所大学设置的体育学科具有综合性特点,体育学研究更多地体现出相关母学科理论在体育学中的运用、侧重指导实践、与科技密切相关、体育科研的国际合作融合度高。得到启示:中国的体育学需加强多学科交叉融合,体育学研究需拓宽国际视野,体育科研需加强国际合作,进一步加强中国高校体育学科人才的培养。  相似文献   
3.
In the whole world, the internet is exercised by millions of people every day for information retrieval. Even for a small to smaller task like fixing a fan, to cook food or even to iron clothes persons opt to search the web. To fulfill the information needs of people, there are billions of web pages, each having a different degree of relevance to the topic of interest (TOI), scattered throughout the web but this huge size makes manual information retrieval impossible. The page ranking algorithm is an integral part of search engines as it arranges web pages associated with a queried TOI in order of their relevance level. It, therefore, plays an important role in regulating the search quality and user experience for information retrieval. PageRank, HITS, and SALSA are well-known page ranking algorithm based on link structure analysis of a seed set, but ranking given by them has not yet been efficient. In this paper, we propose a variant of SALSA to give sNorm(p) for the efficient ranking of web pages. Our approach relies on a p-Norm from Vector Norm family in a novel way for the ranking of web pages as Vector Norms can reduce the impact of low authority weight in hub weight calculation in an efficient way. Our study, then compares the rankings given by PageRank, HITS, SALSA, and sNorm(p) to the same pages in the same query. The effectiveness of the proposed approach over state of the art methods has been shown using performance measurement technique, Mean Reciprocal Rank (MRR), Precision, Mean Average Precision (MAP), Discounted Cumulative Gain (DCG) and Normalized DCG (NDCG). The experimentation is performed on a dataset acquired after pre-processing of the results collected from initial few pages retrieved for a query by the Google search engine. Based on the type and amount of in-hand domain expertise 30 queries are designed. The extensive evaluation and result analysis are performed using MRR, [email protected], MAP, DCG, and NDCG as the performance measuring statistical metrics. Furthermore, results are statistically verified using a significance test. Findings show that our approach outperforms state of the art methods by attaining 0.8666 as MRR value, 0.7957 as MAP value. Thus contributing to the improvement in the ranking of web pages more efficiently as compared to its counterparts.  相似文献   
4.
We evaluate author impact indicators and ranking algorithms on two publication databases using large test data sets of well-established researchers. The test data consists of (1) ACM fellowship and (2) various life-time achievement awards. We also evaluate different approaches of dividing credit of papers among co-authors and analyse the impact of self-citations. Furthermore, we evaluate different graph normalisation approaches for when PageRank is computed on author citation graphs.We find that PageRank outperforms citation counts in identifying well-established researchers. This holds true when PageRank is computed on author citation graphs but also when PageRank is computed on paper graphs and paper scores are divided among co-authors. In general, the best results are obtained when co-authors receive an equal share of a paper's score, independent of which impact indicator is used to compute paper scores. The results also show that removing author self-citations improves the results of most ranking metrics. Lastly, we find that it is more important to personalise the PageRank algorithm appropriately on the paper level than deciding whether to include or exclude self-citations. However, on the author level, we find that author graph normalisation is more important than personalisation.  相似文献   
5.
近几年,国内外大学排行榜呈现多样化趋势,社会关注度普遍提升。大学排行榜对高校发展发挥了重要作用。本文选取国内外8 个大学排行榜作为研究对象进行公信度研究,从权威性和客观性、可信赖度、社会关心程度、影响力4个维度,具体采用9个测评指标,运用主成分分析法等多角度研究构建大学排行榜公信度测评指标体系。以期为我国“双一流”建设、大学内涵式发展、学生与家长择校保障等方面提供一种有效的方法。  相似文献   
6.
评价对大学的发展具有积极作用。所谓大学评价排名化,主要指排名成为一些大学评价结果的唯一表现形式,参与大学排名的机构愈来愈多,大学排名涉及的领域愈来愈广。值得思考的问题是,这么多的大学排名是否必要?大学排名的评价指标是否合理?所谓大学评价国际化,主要指21世纪之后流行的世界大学排名的实质是评价机构用一个尺度或者说一个国际性的尺度去评价不同国家的大学。四大排行榜已经对一些国家政府的高等教育政策、众多大学的办学理念、方向和行为、普通大众对高等教育的认识等产生了不可低估的影响。世界大学排名评价的科研偏好、英语偏好、理科偏好的特征是值得认真关注的。  相似文献   
7.
袁曦临  戴琦  宋歌 《科研管理》2019,40(11):12-21
旨在揭示双一流大学学科战略规划对学科发展与创新的影响。选择从群体行为视角,探讨我国大学双一流建设规划的隐性逻辑及其存在风险。研究表明,标杆管理是中国大学学科规划的基本思路,大学排名及其指标体系成为锁定学科发展方向的重要工具,由此带来学科建设的马太效应和内卷化风险,而学科的多样性与均衡发展,才是双一流大学建设的根本。  相似文献   
8.
大学排名自产生以来一直以来备受诟病,备受争议的背后实质上反映出社会公众在对待大学排名方面所体现出的一元、片面的认识,并没有进行充分且辩证的思考。实际上大学排名无论是对社会还是高校的发展,都能够产生一定的积极作用。文章采用二维象限分析法,建构新的研究模型,将大学排名的影响进行详细的整理与归类,归纳出大学排名对两大主体:大学与社会所带来的积极、消极作用,如大学排名能够促进大学管理制度的改革与完善、大学排名对于不同群体的学生选择大学就读具有差异性等。因此,使用二维象限分析法对大学排名的影响进行逐一的分析,有利于从大学与社会的角度打破社会公众对大学排名原有的刻板印象,从而全面地认识大学排名的影响。  相似文献   
9.
This paper proposes a simple, flexible, axiom-based mechanism for facilitating the comparison between a scholar's citation count and the visibility of the journals wherein the scholar's articles were published. The goal is to help research review bodies easily grasp the distinction these two forms of scholarly accomplishment and to provide a transparent way to articulate expectations about them to scholars. The approach is demonstrated using a widely applied and cooperative functional form that can reflect, via different parameter values, a wide range of possible beliefs about the relative merits of citation counts and journal visibility.  相似文献   
10.
As the volume of scientific articles has grown rapidly over the last decades, evaluating their impact becomes critical for tracing valuable and significant research output. Many studies have proposed various ranking methods to estimate the prestige of academic papers using bibliometric methods. However, the weight of the links in bibliometric networks has been rarely considered for article ranking in existing literature. Such incomplete investigation in bibliometric methods could lead to biased ranking results. Therefore, a novel scientific article ranking algorithm, W-Rank, is introduced in this study proposing a weighting scheme. The scheme assigns weight to the links of citation network and authorship network by measuring citation relevance and author contribution. Combining the weighted bibliometric networks and a propagation algorithm, W-Rank is able to obtain article ranking results that are more reasonable than existing PageRank-based methods. Experiments are conducted on both arXiv hep-th and Microsoft Academic Graph datasets to verify the W-Rank and compare it with three renowned article ranking algorithms. Experimental results prove that the proposed weighting scheme assists the W-Rank in obtaining ranking results of higher accuracy and, in certain perspectives, outperforming the other algorithms.  相似文献   
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