Information and Communication Technologies and Informal Scholarly Communication: A Review of the Social Oriented Research

This article reviews and analyzes findings from research on computer mediated informal scholarly communication. Ten empirical research papers, which show the effects and influences of information & communication technologies (ICTs), or the effects of social contexts on ICTs use in informal scholarly communication, were analyzed and compared. Types of ICTs covered in those studies include e-mails, collaboratories, and electronic forums. The review shows that most of the empirical studies examined the ICTs use effects or consequences. Only a few studies examined the social shaping of ICTs and ICT uses in informal scholarly communication. Based on comparisons of the empirical findings this article summarizes the ICT use effects/consequences as identified in the studies into seven categories and discusses their implications

Defining and Estimating Intervention Effects for Groups that will Develop an Auxiliary Outcome

It has recently become popular to define treatment effects for subsets of the target population characterized by variables not observable at the time a treatment decision is made. Characterizing and estimating such treatment effects is tricky; the most popular but naive approach inappropriately adjusts for variables affected by treatment and so is biased. We consider several appropriate ways to formalize the effects: principal stratification, stratification on a single potential auxiliary variable, stratification on an observed auxiliary variable and stratification on expected levels of auxiliary variables. We then outline identifying assumptions for each type of estimand. We evaluate the utility of these estimands and estimation procedures for decision making and understanding causal processes, contrasting them with the concepts of direct and indirect effects. We motivate our development with examples from nephrology and cancer screening, and use simulated data and real data on cancer screening to illustrate the estimation methods.Comment: Published at http://dx.doi.org/10.1214/088342306000000655 in the Statistical Science (http://www.imstat.org/sts/) by the Institute of Mathematical Statistics (http://www.imstat.org

A Clustering-Based Algorithm for Data Reduction

Finding an efficient data reduction method for large-scale problems is an imperative task. In this paper, we propose a similarity-based self-constructing fuzzy clustering algorithm to do the sampling of instances for the classification task. Instances that are similar to each other are grouped into the same cluster. When all the instances have been fed in, a number of clusters are formed automatically. Then the statistical mean for each cluster will be regarded as representing all the instances covered in the cluster. This approach has two advantages. One is that it can be faster and uses less storage memory. The other is that the number of new representative instances need not be specified in advance by the user. Experiments on real-world datasets show that our method can run faster and obtain better reduction rate than other methods