Work group inclusion : test of a scale and model

We develop a theoretically based 10-item measure of work group inclusion comprised of two components (belongingness and uniqueness) and use this measure to empirically test the nomological network of work group inclusion developed by Shore et al. In Phase 1, we use two samples of full-time employees to develop and refine items as well as establish content validity. In Phase 2, we demonstrate convergent, discriminant, and incremental validity with both conceptually related and unrelated constructs. In Phase 3, we use data from an additional sample of employees and supervisors to test criterion-related validity and mediation by examining the multilevel relationships between inclusion and important antecedents and outcomes. Across the three phases of our study, the results demonstrate support not only for the factor structure, reliability, and validity of our work group inclusion measure but also for a theoretical model in which the construct of inclusion has important implications for individuals and organizations

Fuzzy Lattice Reasoning for Pattern Classification Using a New Positive Valuation Function

This paper describes an enhancement of fuzzy lattice reasoning (FLR) classifier for pattern classification based on a positive valuation function. Fuzzy lattice reasoning (FLR) was described lately as a lattice data domain extension of fuzzy ARTMAP neural classifier based on a lattice inclusion measure function. In this work, we improve the performance of FLR classifier by defining a new nonlinear positive valuation function. As a consequence, the modified algorithm achieves better classification results. The effectiveness of the modified FLR is demonstrated by examples on several well-known pattern recognition benchmarks

Inclusive or Not?: Development of a Student Survey to Measure Students’ Perspectives of Teacher and Leader Abilities to Lead Racially Diverse Schools

This research highlights the development of a survey that measures students’ perspectives and the powerful role they play in measuring teachers’ and leaders’ practices for school inclusion in an urban school environment. Using an exploratory student survey, students were surveyed regarding their perspectives of their principals’ and teachers’ abilities to lead a school with changing demographics. Exploratory Factor Analysis, Confirmatory Factor Analysis, and Rasch analysis were used to generate a good fit of the survey constructs, test if measures of the constructs were consistent with the anticipated dimensionality of an inclusion scale and to determine reliability and validity. Overall, the student survey results reflected low inclusion measures for teachers and leaders. The inclusion measure for leaders was much lower than the teacher inclusion measure. The findings suggested students believe their teachers and leaders are not equipped in creating an inclusionary environment for a racially diverse campus. Some students felt their principals were not fair in how they disciplined students of color. Students believed there were concerns about how their parents were treated when they came to the school. These students also believed their schools were not supportive in preparing them for post-secondary programs. By surveying students, the researcher collected data that informed leaders and teachers about how students truly feel about their school regarding inclusivity. The researcher anticipates this study will change practices of both teachers and leaders in schools with changing demographics

Using Machine Learning to Predict the Evolution of Physics Research

The advancement of science as outlined by Popper and Kuhn is largely qualitative, but with bibliometric data it is possible and desirable to develop a quantitative picture of scientific progress. Furthermore it is also important to allocate finite resources to research topics that have growth potential, to accelerate the process from scientific breakthroughs to technological innovations. In this paper, we address this problem of quantitative knowledge evolution by analysing the APS publication data set from 1981 to 2010. We build the bibliographic coupling and co-citation networks, use the Louvain method to detect topical clusters (TCs) in each year, measure the similarity of TCs in consecutive years, and visualize the results as alluvial diagrams. Having the predictive features describing a given TC and its known evolution in the next year, we can train a machine learning model to predict future changes of TCs, i.e., their continuing, dissolving, merging and splitting. We found the number of papers from certain journals, the degree, closeness, and betweenness to be the most predictive features. Additionally, betweenness increases significantly for merging events, and decreases significantly for splitting events. Our results represent a first step from a descriptive understanding of the Science of Science (SciSci), towards one that is ultimately prescriptive.Comment: 24 pages, 10 figures, 4 tables, supplementary information is include

Measuring Poverty and Economic Inclusion

The current U.S. poverty measure is outdated and has failed to keep up with public consensus on the minimum amount of income needed to "get along" in the United States in the 21st Century. One potential approach to revising the measure, based on recommendations made by a National Academy of Sciences panel in 1995, improves in some ways on the current measure, but has serious limitations of its own that require further research before it is adopted. Moreover, the NAS approach results in a poverty measure that would remain far below the public's get-along level. To address these problems, the incoming Administration should adopt a "tiered" poverty and economic inclusion measure that is modeled on the child poverty measure adopted in 2003 by the United Kingdom

Interval-valued and intuitionistic fuzzy mathematical morphologies as special cases of L-fuzzy mathematical morphology

Mathematical morphology (MM) offers a wide range of tools for image processing and computer vision. MM was originally conceived for the processing of binary images and later extended to gray-scale morphology. Extensions of classical binary morphology to gray-scale morphology include approaches based on fuzzy set theory that give rise to fuzzy mathematical morphology (FMM). From a mathematical point of view, FMM relies on the fact that the class of all fuzzy sets over a certain universe forms a complete lattice. Recall that complete lattices provide for the most general framework in which MM can be conducted. The concept of L-fuzzy set generalizes not only the concept of fuzzy set but also the concepts of interval-valued fuzzy set and Atanassov’s intuitionistic fuzzy set. In addition, the class of L-fuzzy sets forms a complete lattice whenever the underlying set L constitutes a complete lattice. Based on these observations, we develop a general approach towards L-fuzzy mathematical morphology in this paper. Our focus is in particular on the construction of connectives for interval-valued and intuitionistic fuzzy mathematical morphologies that arise as special, isomorphic cases of L-fuzzy MM. As an application of these ideas, we generate a combination of some well-known medical image reconstruction techniques in terms of interval-valued fuzzy image processing

Identification of Group Changes in Blogosphere

The paper addresses a problem of change identification in social group evolution. A new SGCI method for discovering of stable groups was proposed and compared with existing GED method. The experimental studies on a Polish blogosphere service revealed that both methods are able to identify similar evolution events even though both use different concepts. Some differences were demonstrated as wellComment: The 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, IEEE Computer Society, 2012, pp. 1233-123