走出“低谷”的误区——图书馆学发展观评析

指出目前某些人对图书馆学发展处于“低谷”的错误认识 ,分析错误认识的产生原因 ,说明走出“低谷”误区的方法和途径 ,规划图书馆学发展的未来。     

变革时期图书馆学基础理论的任务

对于图书馆学尤其图书馆学基础理论的发展,我们曾一厢情愿地希望它能持续繁荣。一旦出现了理论的“低谷”,我们就要探寻“低谷”出现的原因,并开出帮助理论走出低谷的药方。出于对1982—1987年的理论繁荣局面的怀念,我在1988年以后对图书馆学基础理论低迷状况的原因也进行过较长时间的思考。直到1994年,我才无奈地承认了一个即使依常识判断也很容易认同的想法:只有当图书馆学的发展到了需要确立先进的图书馆学思想时,图书馆     

应从发展战略的角度看“低谷论”

黄宗忠先生一番“低谷”议论,在图书馆界引起如此反响。谈“低谷”问题,先得涉及黄先生几篇论述“低谷”的论文。我的看法是: 一、“低谷”文功不可没大约自1985年下半年起,在图书馆学基础研究领域,一批有资历、有影响的理论家们暂别了理论前沿。一时间,这批理论家们的文章,尤其那种对某一问题进行的系统而严     

稳重与低谷

黄宗忠老师的“低谷论”出台已经一年有半了,赞同者有之,批评者亦有之。作为一名图书馆学的新兵,对于长者的话自然是思之又思。站在更高的角度上看“低谷论”,我们绝不能从个人或职业的感情出发,而应再     

论图书馆学研究对象的确定

目前图书馆学对象研究中存在把"现象"当作"研究对象"和同语反复等错误认识.指出采用逻辑方法、系统方法和哲学方法是纠正上述错误认识的最佳方案,尽快确定图书馆学研究对象是当前图书馆学界的重要任务.     

比较图书馆学散论

比较图书馆学是上个世纪中期以后发展起来的图书馆学分支学科.文章从学科身份、在学科体系中的位置、相关的研究评价、走入低谷的原因四个方面对比较图书馆学提出了一些看法.     

试论传统文化与图书馆

１引言近十多年来,我国图书馆事业,尽管有过不少坑坑洼洼,有过“高峰”与“低谷”,但总体看,发展是迅速的。图书馆的服务设施、管理体制日趋完善,尤其是图书馆学的理论建设,基本跟上了世界形势。然而,不论是理论工作者,亦或是实践工作者,都难以解释这一现象——...     

理论联系实际是图书馆学发展的基础

文章针对某些作者对图书馆学研究存在的错误认识，以国外图书馆学研究为例，指出买践研究是图书馆学研究中的重要内容，重理论轻买践的危害性，坚持理论联系买际的重要性，同时指出图书馆学的研究对象买质上就是图书馆工作。     

近年来图书馆学基础理论研究若干热点问题述评

本文对近年来图书馆学基础理论研究中的“图书馆学研究对象”、“图书馆哲学”和“图书馆学发展”等三个热点进行了述评,并指出图书馆学基础理论研究的两个突出特点是多元化与分散化。文章认为,弱化“研究分散化”特征,预防“研究多元化”光环下过泛过滥的所谓争鸣,使学术研究有效聚焦才能最终推动图书馆学基础理论研究的发展。     

图书馆人文精神:从理论走向实践

进入新世纪后，作为实用图书馆学的人文图书馆学获得了迅速的发展，人文图书馆学、制度图书馆学和技术图书馆学的迅猛发展，使新世纪之初的图书馆学研究形成一个高潮。如果我们将此期的图书馆学研究与20年前图书馆学研究高潮比较，就会发现它们的一些相同之处：两次高潮皆是以实用图书馆学为主力，两次高潮的出现都有着深刻的社会变革背景和理论自身发展的需求。但是两次高潮亦存在明显的差别：1986年前后的“发展战略研究”和“文献资源建设研究”均以效率和效益为价值取向，而当前的“人文图书馆学”和“制度图书馆学”研究则是以人文和制度为基本价值取向.     

Learning to rank for<Emphasis Type="Italic"> why</Emphasis>-question answering

In this paper, we evaluate a number of machine learning techniques for the task of ranking answers to why-questions. We use TF-IDF together with a set of 36 linguistically motivated features that characterize questions and answers. We experiment with a number of machine learning techniques (among which several classifiers and regression techniques, Ranking SVM and SVM ^map ) in various settings. The purpose of the experiments is to assess how the different machine learning approaches can cope with our highly imbalanced binary relevance data, with and without hyperparameter tuning. We find that with all machine learning techniques, we can obtain an MRR score that is significantly above the TF-IDF baseline of 0.25 and not significantly lower than the best score of 0.35. We provide an in-depth analysis of the effect of data imbalance and hyperparameter tuning, and we relate our findings to previous research on learning to rank for Information Retrieval.     

Freedom of information and ‘vexatious’ requests — The case of Scottish local government

This paper investigates the cost and incidence of Freedom of Information (FOI) requests within councils in Scotland and in particular, the cost and incidence of requests which have been defined as ‘vexatious’ in order to investigate if the negative perceptions surrounding the cost and misuse of the legislation are justified. Additionally, the criteria and guidelines that councils are using to define ‘vexatious’ are also examined. The approach taken to the research in this study is a survey of the 32 councils in Scotland using freedom of information requests as the data collection method.     

Librarians' responses to “reading at risk”: A Delphi study

This study surveys librarians' reactions to the National Endowment for the Arts (NEA) report on “Reading at Risk”. Using census data, the NEA concluded that there has been a drastic decline in “literary” reading and it is most sharply pronounced in the youngest group surveyed. To determine whether librarians' perceptions of reading, especially among youth, were consistent with the NEA findings, a Delphi study was conducted. Eleven youth library professionals and educators, identified as experts, responded to survey questions probing whether digital media impact leisure time reading for both adults and adolescents; whether reading is at risk; and whether the decline in literary reading foreshadows erosion in cultural and civic participation. Library experts tended to agree with the effects of digital media on leisure time reading for children. Experts were split on how digital media impact leisure time reading among adult readers. They were closely aligned against the assertion that participation in literary reading foreshadows participation in cultural and civic participation.     

Waves: a fast multi-tier top-<Emphasis Type="Italic">k</Emphasis> query processing algorithm

In this paper, we present Waves, a novel document-at-a-time algorithm for fast computing of top-k query results in search systems. The Waves algorithm uses multi-tier indexes for processing queries. It performs successive tentative evaluations of results which we call waves. Each wave traverses the index, starting from a specific tier level i. Each wave i may insert only those documents that occur in that tier level into the answer. After processing a wave, the algorithm checks whether the answer achieved might be changed by successive waves or not. A new wave is started only if it has a chance of changing the top-k scores. We show through experiments that such lazy query processing strategy results in smaller query processing times when compared to previous approaches proposed in the literature. We present experiments to compare Waves’ performance to the state-of-the-art document-at-a-time query processing methods that preserve top-k results and show scenarios where the method can be a good alternative algorithm for computing top-k results.     

基于网站链接分析的“211工程”高校排名实证研究

阐述基于网站链接分析的高校评价研究中存在的两点不足,即链接同等重要假设和数据不可靠性。然后设计一种可根据不同网站类型有侧重抓取的广度优先爬虫算法,抓取“211工程”高校网站作为研究样本。基于所得数据构成的社会网络,对社会网络分析中节点重要性测度的3种指标的排名效果进行实验,发现邻近度声望指标最优。并进一步引入PageRank思想,提出一种对邻近度声望指标进行改进的新指标UnivRank。实验结果表明,新指标的效果显著优于其他指标。     

(<Emphasis Type="Italic">CF</Emphasis>)<Superscript>2</Superscript> architecture: contextual collaborative filtering

Recommender systems have dramatically changed the way we consume content. Internet applications rely on these systems to help users navigate among the ever-increasing number of choices available. However, most current systems ignore the fact that user preferences can change according to context, resulting in recommendations that do not fit user interests. This research addresses these issues by proposing the \(({ CF})^2\) architecture, which uses local learning techniques to embed contextual awareness into collaborative filtering models. The proposed architecture is demonstrated on two large-scale case studies involving over 130 million and over 7 million unique samples, respectively. Results show that contextual models trained with a small fraction of the data provided similar accuracy to collaborative filtering models trained with the complete dataset. Moreover, the impact of taking into account context in real-world datasets has been demonstrated by higher accuracy of context-based models in comparison to random selection models.     

Earnings & education of men & women in selected media occupations

Using basic census information, and manipulating that data to show sexual differences in rates of pay, the following brief note provides a statistical basis for some commonly‐held assumptions on the low‐pay status of women. The author is professor and Dean of the School of Communications and Theater at Temple University in Philadelphia.     

Not by technology alone: The “analog” aspects of online public engagement in policymaking

Between Twitter revolutions and Facebook elections, there is a growing belief that information and communication technologies are changing the way democracy is practiced. The discourse around e-government and online deliberation is frequently focused on technical solutions and based in the belief that if you build it correctly they will come. This paper departs from the literature on digital divide to examine barriers to online civic participation in policy deliberation. While most scholarship focuses on identifying and describing those barriers, this study offers an in-depth analysis of what it takes to address them using a particular case study. Based in the tradition of action research, this paper focuses on analysis of practices that evolved in Regulation Room—a research project of CeRI (Cornell eRulemaking Initiative) that works with federal government agencies in helping them engage public in complex policymaking processes. It draws a multidimensional picture of motivation, skill, and general political participation divides; or the “analog” aspects of the digital divide in online civic participation and policy deliberation.     

<Emphasis Type="Italic">EveTAR</Emphasis>: building a large-scale multi-task test collection over Arabic tweets

This article introduces a new language-independent approach for creating a large-scale high-quality test collection of tweets that supports multiple information retrieval (IR) tasks without running a shared-task campaign. The adopted approach (demonstrated over Arabic tweets) designs the collection around significant (i.e., popular) events, which enables the development of topics that represent frequent information needs of Twitter users for which rich content exists. That inherently facilitates the support of multiple tasks that generally revolve around events, namely event detection, ad-hoc search, timeline generation, and real-time summarization. The key highlights of the approach include diversifying the judgment pool via interactive search and multiple manually-crafted queries per topic, collecting high-quality annotations via crowd-workers for relevancy and in-house annotators for novelty, filtering out low-agreement topics and inaccessible tweets, and providing multiple subsets of the collection for better availability. Applying our methodology on Arabic tweets resulted in EveTAR, the first freely-available tweet test collection for multiple IR tasks. EveTAR includes a crawl of 355M Arabic tweets and covers 50 significant events for which about 62K tweets were judged with substantial average inter-annotator agreement (Kappa value of 0.71). We demonstrate the usability of EveTAR by evaluating existing algorithms in the respective tasks. Results indicate that the new collection can support reliable ranking of IR systems that is comparable to similar TREC collections, while providing strong baseline results for future studies over Arabic tweets.     

Measuring Success in the “Congo Gorilla Forest” Conservation Exhibition

Abstract Most zoo visitors are primarily motivated by the joys of watching animals, which may preclude attention to major ecological issues that are the focus of research in biodiversity, habitats, and other matters pertaining to the survival of wild animals. The Wildlife Conservation Society exhibition Congo Gorilla Forest is a popular animal‐watching experience, but it also communicates considerable educational content, stimulating visitors' interest in and awareness of ecological relationships. This article reviews the phases of an evaluation process that assisted WCS staff in making decisions about exhibition design and interpretation; it discusses measurement challenges in assessing outcomes; and it uses key findings from the evaluation process to define and explain the interpretive success of the project. Success for this conservation exhibition is described in terms of achieving three educational goals while recognizing the diversity among audiences. The exhibition's effectiveness is attributed to understanding visitors' expectations and interests, creating an array of exhibit formats to engage people, and communicating conservation messages visually and experientially.