Measuring research impact: A first approximation of the achievements of the iSchools in ISI's information and library science category ??? An exploratory study

In this paper, we analyze those publications of the home institutes of the iSchools that are indexed by Thomson Reuters (ISI) Web of Science in the information science and library science category, and were published between 2000 and 2009

Comparing Typical Opening Move Choices Made by Humans and Chess Engines

The opening book is an important component of a chess engine, and thus computer chess programmers have been developing automated methods to improve the quality of their books. For chess, which has a very rich opening theory, large databases of high-quality games can be used as the basis of an opening book, from which statistics relating to move choices from given positions can be collected. In order to find out whether the opening books used by modern chess engines in machine versus machine competitions are ``comparable'' to those used by chess players in human versus human competitions, we carried out analysis on 26 test positions using statistics from two opening books one compiled from humans' games and the other from machines' games. Our analysis using several nonparametric measures, shows that, overall, there is a strong association between humans' and machines' choices of opening moves when using a book to guide their choices.Comment: 12 pages, 1 figure, 6 table

A scientometric look at scholory cooperation between Europe and Israel. An explorative study of a changing landscape.

Cooperation; Studies;

Attentes versus réalité

Les chercheurs qui analysent le Web s’appuient sur des données qui sont souvent collectées à l’aide des moteurs de recherche. Dans une précédente contribution (Bar-Ilan, 2005), l’auteur a proposé une liste d’objectifs pour le moteur de recherche idéal en expliquant le besoin de fonctionnalités spécifiques pour ce type d’activité. Ici, il revisite cette liste et examine si les principaux moteurs de recherche actuels peuvent répondre, au moins partiellement, aux exigences de l’outil de recherche idéal. Les principaux outils de recherche sont commerciaux et destinés à l’utilisateur « moyen » et non au chercheur scientifique qui analyse le Web, ils ne peuvent donc pas satisfaire toutes les demandes.Web research is based on data from the Web. Often data is collected using search engines. In a previous paper (Bar-Ilan, 2005) we proposed a “wish list” for the ideal search engine and explained the need for specific features. In this paper we revisit this list and examine whether the currently existing major search engines can at least partially fulfil the requirements of the ultimate search tool. The major search tools are commercial and are oriented towards the “average” user and not towards the Web researcher, and therefore are unable to meet all the requests

A Markov chain model for changes in users’ assessment of search results

Previous research shows that users tend to change their assessment of search results over time. This is a first study that investigates the factors and reasons for these changes, and describes a stochastic model of user behaviour that may explain these changes. In particular, we hypothesise that most of the changes are local, i.e. between results with similar or close relevance to the query, and thus belong to the same ”coarse” relevance category. According to the theory of coarse beliefs and categorical thinking, humans tend to divide the range of values under consideration into coarse categories, and are thus able to distinguish only between cross-category values but not within them. To test this hypothesis we conducted five experiments with about 120 subjects divided into 3 groups. Each student in every group was asked to rank and assign relevance scores to the same set of search results over two or three rounds, with a period of three to nine weeks between each round. The subjects of the last three-round experiment were then exposed to the differences in their judgements and were asked to explain them. We make use of a Markov chain model to measure change in users’ judgments between the different rounds. The Markov chain demonstrates that the changes converge, and that a majority of the changes are local to a neighbouring relevance category. We found that most of the subjects were satisfied with their changes, and did not perceive them as mistakes but rather as a legitimate phenomenon, since they believe that time has influenced their relevance assessment. Both our quantitative analysis and user comments support the hypothesis of the existence of coarse relevance categories resulting from categorical thinking in the context of user evaluation of search results