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The purpose of this paper is to report on a qualitative study that examined how pre-service teachers (PSTs) used mobile technology and experiential learning to critically examine the processes that shape places over time. During Summer course work that occurred prior to beginning their field experience and student teaching, participants explored neighborhoods and public spaces, and researched the history as well as contemporary issues relevant to the places in which their future students live, play, work, shop, and go to school. The use of social media as a forum for sharing and reflecting upon their experiences provided opportunity to critique neoliberal and race-based public policies, as well as support reflection on the relationships between geography and teaching about social (in)justice in the social studies. Findings inform the work of teacher educators who seek to help teacher candidates think more deeply about how spatial contexts inform culturally sustaining and critically minded pedagogy in the social studies.

This qualitative study included pre- and post-surveys and two one-on-one interviews between research participants and the researcher. Data were also gathered through the use of posts made by participants to a shared social media account. Interested in the interactive process of subjects and their surroundings, symbolic interactionism provided the methodological framework for this study.

Involvement in the study provided PSTs with new ways of thinking about how places are shaped over time and the importance of incorporating local intersections of geography and injustice in the classroom. Through experiential learning, PSTs developed a critical understanding of how place relates to who they teach, moved away from deficit thinking about people and places, and, as evidenced in the examples shared, approached lesson planning as place-relevant and culturally sustaining social studies educators.

The majority of students enrolled in teacher education courses in the USA remains white and it is well documented that most possess few cultural and geographic ties to the schools and students they work with as PSTs. Interested in the intersection of race, place, and teacher education, this paper discusses research conducted with 12 pre-service secondary social studies teachers (PSTs) who were enrolled in an eight-week Summer seminar course that preceded their Fall field experience and Spring student teaching placements to learn how they interpret their movement through spaces and their understanding of how geography, race, and agency intersect and impact students.

This chapter discusses teaching strategies designed to help students develop an open-minded and critically self-reflective worldview. By attending to the perceptual dimension of global citizenship education, students and instructors begin the important work of reflecting on the deeper influences that consciously or unconsciously influence one’s global perspective. Research suggests that these goals are best achieved through cross-cultural learning experiences that involve people of different backgrounds. The cross-cultural learning experiences discussed in this chapter include meeting with local residents who moved to the United States within the last decade and who now send their children to the schools that students enrolled in teacher education courses would teach in. Additionally, technology was used to connect graduate students seeking their teaching license in the United States with graduate students and teachers in Durban, South Africa as part of an ongoing reflection on how one develops perspective consciousness. The learning activities described below align with the tenets of global education because they are not specific to one discipline or content area but rather focus on ways to develop habits of mind, perspective consciousness, cross-cultural learning opportunities, and a sense of responsibility as aspiring educators that are applicable across the sciences, arts, and humanities.

The aim of this paper is to propose a conceptual model of the field of informetrics. Specifically, the paper argues that informetrics comprises the study of entities in three dimensions: the social, documentary and epistemic dimensions containing respectively agents, documents, and concepts or cognitions.

The paper outlines a conceptual model, drawing on earlier work by Kochen, Leydesdorff, Borgman and others. Subsequently, each dimension and interdimensional relation is analyzed and discussed.

It is shown that not every study necessarily involves each of the three dimensions, but that the field as a whole cannot be reduced to one or two of them. Moreover, the dimensions should be kept separate but they are not completely independent. The paper discusses what kinds of relations exist between the dimensions. Special attention is given to the nature of the citation relation within this framework. The paper also considers the place of concepts like mapping, proximity and influence in the model.

This conceptual paper is a first step. Multi‐relational networks may be a key instrument to further the study of the interplay between the three dimensions.

The paper provides a framework to characterise informetric studies and makes the characteristics of the field explicit.

Syntax-based text classification (TC) mechanisms have been overtly replaced by semantic-based systems in recent years. Semantic-based TC systems are particularly useful in those scenarios where similarity among documents is computed considering semantic relationships among their terms. Kernel functions have received major attention because of the unprecedented popularity of SVMs in the field of TC. Most of the kernel functions exploit syntactic structures of the text, but quite a few also use a priori semantic information for knowledge extraction. The purpose of this paper is to investigate semantic kernel functions in the context of TC.

This work presents performance and accuracy analysis of seven semantic kernel functions (Semantic Smoothing Kernel, Latent Semantic Kernel, Semantic WordNet-based Kernel, Semantic Smoothing Kernel having Implicit Superconcept Expansions, Compactness-based Disambiguation Kernel Function, Omiotis-based S-VSM semantic kernel function and Top-k S-VSM semantic kernel) being implemented with SVM as kernel method. All seven semantic kernels are implemented in SVM-Light tool.

Performance and accuracy parameters of seven semantic kernel functions have been evaluated and compared. The experimental results show that Top-k S-VSM semantic kernel has the highest performance and accuracy among all the evaluated kernel functions which make it a preferred building block for kernel methods for TC and retrieval.

A combination of semantic kernel function with syntactic kernel function needs to be investigated as there is a scope of further improvement in terms of accuracy and performance in all the seven semantic kernel functions.

This research provides an insight into TC using a priori semantic knowledge. Three commonly used data sets are being exploited. It will be quite interesting to explore these kernel functions on live web data which may test their actual utility in real business scenarios.

Comparison of performance and accuracy parameters is the novel point of this research paper. To the best of the authors’ knowledge, this type of comparison has not been done previously.