基于ARWU的世界大学排名体系比较及实证研究

[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。     

大学排名：功能及其局限

大学排名在一定程度上反映了高等教育的质量,满足了公众对大学透明度和信息的需求。但同时,大学排名也可能诱使高校趋同发展,产生片面的导向。改进大学排名应明确排名的目的、作用;使用多渠道数据全面评价;将评价指标重心落在产出上;对排名进行分类,使用有差别的指标体系;建立适当的监督或顾问机构。     

An efficient page ranking approach based on vector norms using sNorm(p) algorithm

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.     

大学排名被曲解误用的困境何以突破？ ——从习近平讲“别太在意排名”谈起

自世界上第一个大学排名出现以来,社会质疑就一直没有停歇过,诋毁多于赞誉。可以说,大学排名是“在骂声中成长的”。为什么诋毁者多呢？概因人们对大学排名的曲解误用,在一定程度上造成了一些社会危害。可是,无论人们喜欢还是不喜欢,大学排名已经是难以消灭的客观存在,并且越来越成为人们认识大学不可或缺的重要工具。讨论大学排名的生存还是死亡,已经没有什么 意义。我们能做的是,认识大学排名存在的合理性,进一步完善大学排名方案、规则与制度,让大学排名依据排名目的更好地服务社会。中国“双一流”建设遴选在即,我们要以“中国特色,世界一流”为宗旨,创建具有中国特色的世界一流大学评价体系。     

从JCR期刊分区看高校学科与科研发展

基于<2009年JCR期刊分区报告>,从学科结构角度,对东华大学2004-2008年被SCIE收录的学术论文进行统计研究,通过对各学科论文产出能力的分析,探讨了东华大学的学科发展现状及趋势.同时基于CSCD收录东华大学2004-2008年发文情况,对比该校国际相对优势学科与国内相对优势学科的差异.论文分析结果对于评价高校学科与科研发展,调整并优化学科建设规划具有可借鉴性.     

Improving unsupervised keyphrase extraction by modeling hierarchical multi-granularity features

Existing unsupervised keyphrase extraction methods typically emphasize the importance of the candidate keyphrase itself, ignoring other important factors such as the influence of uninformative sentences. We hypothesize that the salient sentences of a document are particularly important as they are most likely to contain keyphrases, especially for long documents. To our knowledge, our work is the first attempt to exploit sentence salience for unsupervised keyphrase extraction by modeling hierarchical multi-granularity features. Specifically, we propose a novel position-aware graph-based unsupervised keyphrase extraction model, which includes two model variants. The pipeline model first extracts salient sentences from the document, followed by keyphrase extraction from the extracted salient sentences. In contrast to the pipeline model which models multi-granularity features in a two-stage paradigm, the joint model accounts for both sentence and phrase representations of the source document simultaneously via hierarchical graphs. Concretely, the sentence nodes are introduced as an inductive bias, injecting sentence-level information for determining the importance of candidate keyphrases. We compare our model against strong baselines on three benchmark datasets including Inspec, DUC 2001, and SemEval 2010. Experimental results show that the simple pipeline-based approach achieves promising results, indicating that keyphrase extraction task benefits from the salient sentence extraction task. The joint model, which mitigates the potential accumulated error of the pipeline model, gives the best performance and achieves new state-of-the-art results while generalizing better on data from different domains and with different lengths. In particular, for the SemEval 2010 dataset consisting of long documents, our joint model outperforms the strongest baseline UKERank by 3.48%, 3.69% and 4.84% in terms of F1@5, F1@10 and F1@15, respectively. We also conduct qualitative experiments to validate the effectiveness of our model components.     

大学与国际大学排名的关系评析

我国多所高校“退出”国际大学排名这一消息引发舆论的关注。文章分析了国际大学排名的性质,指出国际大学排名指标是排行机构选择的世界大学共性指标,这属于对大学的社会评价,不是对大学的专业评价。要提升我国大学办学质量与竞争力,应建立现代大学制度,推进专业评价。     

图书借阅排行算法研究

目前图书借阅排行的传统算法是仅按借阅次数排序,该算法存在某些热门图书可能会长期占据排行榜前列之不足。论文提出了新的图书借阅排行算法,该算法综合考虑了借阅次数、复本数、入藏日期和最近外借日期等影响因素,在保证了较好推荐效果的情况下,实现了自动淘汰的机制,解决了传统算法的不足。     

Jointly valuing journal visibility and author citation count: An axiomatic approach

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.     

高校图书馆资源配置的排序选择评价模型构建研究

从硬件设施、人力资源、文献资源以及经费投入4方面选择11个指标,通过指标变量的主成分变换处理,以主观赋值评价法、主成分评价法、因子分析评价法、模糊综合评价法、Topsis评价法5种方法的综合评价结果为参照,采用排序选择模型的构建思路,构建图书馆资源配置的评价模型,实现70所高校图书馆资源配置的评价.结果表明,所构建的模型在评价高校图书馆资源配置的排序及分级上与5种方法具有良好的一致性.