How and why physicists and chemists use blogs

This study examined how and why chemists and physicists blog. Two qualitative methods were used: content analysis of blog and “about” pages and in-depth responsive interviews with chemists and physicists who maintain blogs. Analysis of the data yielded several cross-cutting themes that provide a window into how physicists and chemists use their blogs and what value they receive from maintaining a blog and participating in a blogging community. The article concludes with a discussion of implications for supporting scientists’ work

Information systems evaluation methodologies

Due to the prevalent use of Information Systems (IS) in modern organisations nowadays, evaluation research in this field is becoming more and more important. In light of this, a set of rigorous methodologies were developed and used by IS researchers and practitioners to evaluate the increasingly complex IS implementation used. Moreover, different types of IS and different focusing perspectives of the evaluation require the selection and use of different evaluation approaches and methodologies. This paper aims to identify, explore, investigate and discuss the various key methodologies that can be used in IS evaluation from different perspectives, namely in nature (e.g. summative vs. formative evaluation) and in strategy (e.g. goal-based, goal-free and criteria-based evaluation). The paper concludes that evaluation methodologies should be selected depending on the nature of the IS and the specific goals and objectives of the evaluation. Nonetheless, it is also proposed that formative criteria-based evaluation and summative criteria-based evaluation are currently among the most and more widely used in IS research. The authors suggest that the combines used of one or more of these approaches can be applied at different stages of the IS life cycle in order to generate more rigorous and reliable evaluation outcomes

Stochastic accumulation of feature information in perception and memory

It is now well established that the time course of perceptual processing influences the first second or so of performance in a wide variety of cognitive tasks. Over the last20 years, there has been a shift from modeling the speed at which a display is processed, to modeling the speed at which different features of the display are perceived and formalizing how this perceptual information is used in decision making. The first of these models(Lamberts, 1995) was implemented to fit the time course of performance in a speeded perceptual categorization task and assumed a simple stochastic accumulation of feature information. Subsequently, similar approaches have been used to model performance in a range of cognitive tasks including identification, absolute identification, perceptual matching, recognition, visual search, and word processing, again assuming a simple stochastic accumulation of feature information from both the stimulus and representations held in memory. These models are typically fit to data from signal-to-respond experiments whereby the effects of stimulus exposure duration on performance are examined, but response times (RTs) and RT distributions have also been modeled. In this article, we review this approach and explore the insights it has provided about the interplay between perceptual processing, memory retrieval, and decision making in a variety of tasks. In so doing, we highlight how such approaches can continue to usefully contribute to our understanding of cognition

Mapping dynamic interactions among cognitive biases in depression

Depression is theorized to be caused in part by biased cognitive processing of emotional information. Yet, prior research has adopted a reductionist approach that does not characterize how biases in cognitive processes such as attention and memory work together to confer risk for this complex multifactorial disorder. Grounded in affective and cognitive science, we highlight four mechanisms to understand how attention biases, working memory difficulties, and long-term memory biases interact and contribute to depression. We review evidence for each mechanism and highlight time- and context-dependent dynamics. We outline methodological considerations and recommendations for research in this area. We conclude with directions to advance the understanding of depression risk, cognitive training interventions, and transdiagnostic properties of cognitive biases and their interactions

A question of trust: can we build an evidence base to gain trust in systematic review automation technologies?

Background Although many aspects of systematic reviews use computational tools, systematic reviewers have been reluctant to adopt machine learning tools. Discussion We discuss that the potential reason for the slow adoption of machine learning tools into systematic reviews is multifactorial. We focus on the current absence of trust in automation and set-up challenges as major barriers to adoption. It is important that reviews produced using automation tools are considered non-inferior or superior to current practice. However, this standard will likely not be sufficient to lead to widespread adoption. As with many technologies, it is important that reviewers see “others” in the review community using automation tools. Adoption will also be slow if the automation tools are not compatible with workflows and tasks currently used to produce reviews. Many automation tools being developed for systematic reviews mimic classification problems. Therefore, the evidence that these automation tools are non-inferior or superior can be presented using methods similar to diagnostic test evaluations, i.e., precision and recall compared to a human reviewer. However, the assessment of automation tools does present unique challenges for investigators and systematic reviewers, including the need to clarify which metrics are of interest to the systematic review community and the unique documentation challenges for reproducible software experiments. Conclusion We discuss adoption barriers with the goal of providing tool developers with guidance as to how to design and report such evaluations and for end users to assess their validity. Further, we discuss approaches to formatting and announcing publicly available datasets suitable for assessment of automation technologies and tools. Making these resources available will increase trust that tools are non-inferior or superior to current practice. Finally, we identify that, even with evidence that automation tools are non-inferior or superior to current practice, substantial set-up challenges remain for main stream integration of automation into the systematic review process

Overview of the CHIIR 2019 Workshop on Barriers to InteractiveIR Resources Re-use (BIIRRR 2019)

This paper presents an overview of the BIIRRR 2019 workshop at CHIIR 2019, which had the explicit aim of understanding and promoting re-use of resources for interactive IR experimentation

Team Learning: A Theoretical Integration and Review

With the increasing emphasis on work teams as the primary architecture of organizational structure, scholars have begun to focus attention on team learning, the processes that support it, and the important outcomes that depend on it. Although the literature addressing learning in teams is broad, it is also messy and fraught with conceptual confusion. This chapter presents a theoretical integration and review. The goal is to organize theory and research on team learning, identify actionable frameworks and findings, and emphasize promising targets for future research. We emphasize three theoretical foci in our examination of team learning, treating it as multilevel (individual and team, not individual or team), dynamic (iterative and progressive; a process not an outcome), and emergent (outcomes of team learning can manifest in different ways over time). The integrative theoretical heuristic distinguishes team learning process theories, supporting emergent states, team knowledge representations, and respective influences on team performance and effectiveness. Promising directions for theory development and research are discussed

INTERACTIVE INFORMATION RETRIEVAL: AN OVERVIEW

Information Retrieval (IR) is deal as the interface between the information handler and the framework and the query thus formulated is matched against a keyword indexed in the system whereas in Interactive Information Retrieval (IIR) Human Computer Interaction (HCI) is involved. Although, Interactive Information Retrieval (IIR) is a multidisciplinary field its broad subject is Computer science. Algorithmic kind of IR is still prevalent in Interactive Information Retrieval (IIR), information seeking and information behavior

Lessons learned from the CHiC and SBS interactive tracks: a wishlist for interactive IR evaluation

Over the course of the past two decades, the Interactive Tracks at TREC and INEX have contributed greatly to our knowledge of how to run an interactive IR evaluation campaign. In this position paper, we add to this body of knowledge by taking stock of our own experiences and challenges in organizing the CHIC and SBS Interactive Tracks from 2013 to 2016 in the form of a list of important properties of any future IIR evaluation campaigns