共查询到19条相似文献，搜索用时 78 毫秒
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"学校声誉"排名已成为我国大陆大学排行榜的评价指标之一,但目前进行的"学校声誉"排名不符合中国国情,其实质就是"社会声誉"排名。符合中国国情的"学校声誉"包括"社会声誉"、"学术声誉"、"国家声誉"三个方面。本文选取2004年在网大、中国校友会和中国管理科学研究院三家评价机构的学校综合排名中,进入前50 名的并集,共有65所大学为标本,进行学校声誉排名研究,并给出2004年中国大陆50强"学校声誉"排行榜。 相似文献
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世界大学综合竞争力与网络竞争力排名的比较研究 总被引：1，自引：0，他引：1
世界上有许多项大学排名。但各个排名的依据、做法和结果各不相同。为了更好地利用各项排名结果,本文以两种著名的世界大学排名：U.S．News发布的“世界最佳大学”排名和西班牙网络计量实验室发布的“世界大学网络排名”为对象,深人分析和比较两项排名的指标、权重、数据来源和结果,并探讨二者之间的关系。结果表明：世界大学综合竞争力和网络竞争力之间相关性并不高;但是、综合实力表现较好的学校其网络表现不会很落后。且有相当一部分顶尖大学的网络排名亦表现很好;网络排名有一定的特色,是对综合竞争力排名的重要补充,等等。 相似文献
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以CNKI全文数据库为数据源,选择"985工程"建设的39所国内著名大学为样本,统计这些大学1999-2013年间发表学术论文的大学图书馆h指数、大学h指数、大学图书馆核心作者h指数、大学图书馆获取基金项目数量等文献计量指标。对国内大学图书馆做一个排名。在此基础上,对这些计量学指标进行相关性分析,通过比较各类别的计量指标特征,以期能为促进国内大学图书馆学术科研水平的提升提供一些帮助。 相似文献
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近期,中国科学评价研究中心发布了“中国大学网络排名”。此次排名以西班牙国家研究理事会（ConsejoSuperiordeInvestigacionesCientificas,CSIC）人文与社会科学研究中心（Cen＇trodeCienciasHumanasySociales,CCHS）下属的网络计量实验室（CybermetricsLab）发布的“世界大学网络计量排名（2010年1月版）”和武汉大学中国科学评价研究中心发布的“中国重点大学网络影响力排名（2008年）”为基础, 相似文献
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(大学)排名之所以成为一个全球性的现象是因为每所高校都有成为‘世界一流大学’的愿望,越来越多的学生选择出国留学,一些国家把吸引大量海外学生作为增加收入的国家战略,在全球竞争中(大学)排名可以用来吸引最优秀的学生 相似文献
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西班牙网路计量研究中心（CentreforScientificInformationandDocumentation,CINDOC-CSoC）的网路实验室（LaboratoriodeInternet）自2004年起,每年1月及7月均发表“世界大学网路排名”（WebometricsRankingsofWorldUniversities）。此项排名的目的在提升各大学的学术知识与资料于网路上公开出版（Webpublication）的程度,以经济、 相似文献
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利用CSSCI对大学出版社进行了被引统计分析,列出了大学出版社的被引排名以及影响较大的著作,目的在于呼吁大学出版社重视学术,把大学出版社办成学术研究的给氧站。 相似文献
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文章从学术成果产出数量和学术影响力两个维度,对我国30所"985工程"大学图书馆从核心期刊发文数量和分布情况、论文被引数量和频次、论文的基金资助情况和论文国际化程度进行量化分析,并对一流大学图书馆的整体学术能力进行了综合评价和排名。 相似文献
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[目的/意义]"双一流"建设使得大学排名成为社会焦点,但其能否反映高校的真实水平仍存质疑,故探析验证大学排名体系的公正性、合理性意义重大。[方法/过程]以世界大学学术排名（ARWU）为研究对象,在深入探究ARWU指标体系及其构型的基础上,对比其与QS、THES、USNWR等著名世界大学排名体系的差异,并以东南亚主要知名大学为例进行实证研究。[结果/结论] ARWU的排名位次与QS、THES、USNWR有较大差异,存在系统误差的可能性,因而有必要在辩证对待各类大学排名体系的基础上,从指标调整等方面对其进行改进和修正。 相似文献
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Information Retrieval Journal - Ranking models are the main components of information retrieval systems. Several approaches to ranking are based on traditional machine learning algorithms using a... 相似文献
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《Journal of Informetrics》2014,8(3):581-593
Bornmann, Stefaner, de Moya Anegón, and Mutz (2014) have introduced a web application (www.excellencemapping.net) which is linked to both academic ranking lists published hitherto (e.g. the Academic Ranking of World Universities) as well as spatial visualization approaches. The web application visualizes institutional performance within specific subject areas as ranking lists and on custom tile-based maps. The new, substantially enhanced version of the web application and the generalized linear mixed model for binomial data on which it is based are described in this paper. Scopus data are used which have been collected for the SCImago Institutions Ranking. Only those universities and research-focused institutions are considered that have published at least 500 articles, reviews and conference papers in the period 2006–2010 in a certain Scopus subject area. In the enhanced version, the effect of single covariates (such as the per capita GDP of a country in which an institution is located) on two performance metrics (best paper rate and best journal rate) is examined and visualized. A covariate-adjusted ranking and mapping of the institutions is produced in which the single covariates are held constant. The results on the performance of institutions can then be interpreted as if the institutions all had the same value (reference point) for the covariate in question. For example, those institutions can be identified worldwide showing a very good performance despite a bad financial situation in the corresponding country. 相似文献
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A theoretic framework for multimedia information retrieval is introduced which guarantees optimal retrieval effectiveness. In particular, a Ranking Principle for Distributed Multimedia-Documents (RPDM) is described together with an algorithm that satisfies this principle. Finally, the RPDM is shown to be a generalization of the Probability Ranking principle (PRP) which guarantees optimal retrieval effectiveness in the case of text document retrieval. The PRP justifies theoretically the relevance ranking adopted by modern search engines. In contrast to the classical PRP, the new RPDM takes into account transmission and inspection time, and most importantly, aspectual recall rather than simple recall. 相似文献
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对美国图书情报学院前20强的定量分析与实例研究 总被引：13，自引：0，他引：13
文章根据美国图书情报学院的排名结果,从因特网上收集了美国2000年图书情报学院前20强单位的详细资料,经过归类整理后,对院系名称进行了总结,对各校师资力量、研究方向与教学课程设置进行了定量、定性和实例分析,并对美国高校排名统计作了归纳。 相似文献
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Ben Carterette 《Information Retrieval》2011,14(1):89-106
A useful ability for search engines is to be able to rank objects with novelty and diversity: the top k documents retrieved should cover possible intents of a query with some distribution, or should contain a diverse set of subtopics
related to the user’s information need, or contain nuggets of information with little redundancy. Evaluation measures have
been introduced to measure the effectiveness of systems at this task, but these measures have worst-case NP-hard computation
time. The primary consequence of this is that there is no ranking principle akin to the Probability Ranking Principle for
document relevance that provides uniform instruction on how to rank documents for novelty and diversity. We use simulation
to investigate the practical implications of this for optimization and evaluation of retrieval systems. 相似文献
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Christoph Baumgarten 《Information Retrieval》2000,3(3):253-271
This paper describes a probabilistic model for optimum information retrieval in a distributed heterogeneous environment.The model assumes the collection of documents offered by the environment to be partitioned into subcollections. Documents as well as subcollections have to be indexed, where indexing methods using different indexing vocabularies can be employed. A query provided by a user is answered in terms of a ranked list of documents. The model determines a procedure for ranking the documents that stems from the Probability Ranking Principle: For each subcollection, the subcollection's documents are ranked; the resulting ranked lists are combined into a final ranked list of documents, where the ordering is determined by the documents' probabilities of being relevant with respect to the user's query. Various probabilistic ranking methods may be involved in the distributed ranking process. A criterion for effectively limiting the ranking process to a subset of subcollections extends the model.The property that different ranking methods and indexing vocabularies can be used is important when the subcollections are heterogeneous with respect to their content.The model's applicability is experimentally confirmed. When exploiting the degrees of freedom provided by the model, experiments showed evidence that the model even outperforms comparable models for the non-distributed case with respect to retrieval effectiveness. 相似文献
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Ranking information resources is a task that usually happens within more complex workflows and that typically occurs in any form of information retrieval, being commonly implemented by Web search engines. By filtering and rating data, ranking strategies guide the navigation of users when exploring large volumes of information items. There exist a considerable number of ranking algorithms that follow different approaches focusing on different aspects of the complex nature of the problem, and reflecting the variety of strategies that are possible to apply. With the growth of the web of linked data, a new problem space for ranking algorithms has emerged, as the nature of the information items to be ranked is very different from the case of Web pages. As a consequence, existing ranking algorithms have been adapted to the case of Linked Data and some specific strategies have started to be proposed and implemented. Researchers and organizations deploying Linked Data solutions thus require an understanding of the applicability, characteristics and state of evaluation of ranking strategies and algorithms as applied to Linked Data. We present a classification that formalizes and contextualizes under a common terminology the problem of ranking Linked Data. In addition, an analysis and contrast of the similarities, differences and applicability of the different approaches is provided. We aim this work to be useful when comparing different approaches to ranking Linked Data and when implementing new algorithms. 相似文献