Challenging the Gaze: The Subject of Attention and a 1915 Montessori Demonstration Classroom

The child's attention, how this attention is reasoned about, and how attention works as a surface for pedagogical intervention are central to understanding modern schooling. This article examines "attention" as an object of knowledge related to the organization and management of individuals. I address what we might learn about attention by studying one specific Montessori classroom, the glass‐walled public demonstration set up at the 1915 San Francisco World's Fair. The pedagogy of attention on display and the spectatorship of the classroom provide an opportunity to rethink how power and subjectivity play in the formation of human attractions. I argue that thinking through Montessori offers important and relevant suggestions for present-day examinations of attention. The 1915 demonstration classroom can help us theorize the relation of attention to normalizing and governmentalizing practices. This specific study of how attention operates in one locale has implications for tactile learning theories and for the analytics of power to be used in studies of attention.     

Caught inside the black box: Criminalization,opaque technology,and the New York subway MetroCard

Abstract

This article investigates how an account of hidden, internal properties of an everyday technology became a framework to interpret human action as a serious crime. Using a case study situated in the New York subway system, I examine the criminalization of a practice of New York’s poor known as “selling swipes” performed by so-called “swipers”. A high court came to classify the practice as felony forgery, interpreting it though an expert-witness account of how objects physically manipulated by swipers interact with a secretive, proprietary digital information system. Thousands of felony arrests – overwhelmingly of nonwhite men – have been legitimated under this theory, in which the crime occurs on a plane of technical interactions to which swipers have no access. Through close examination of the underlying technology (known as MetroCard), however, I show considerable problems in the authorities’ understanding of the technology, illustrating the hazards of interpreting human action through proprietary or complex systems, especially as they are represented solely through expert accounts. The case demonstrates fresh connections between technology and unequal outcomes in the U.S. criminal justice system, and suggests an emerging form of social vulnerability, to interpretations of our actions through the logic of technologies black-boxed to us.     

The Contribution of HBCUS to the Preparation of African American Women for Stem Careers: A Case Study

This study uses case study analysis to explore the ways that Spelman College, a historically Black women’s college, promotes the attainment of African American women in STEM fields. Although limited to one institution, the findings shed light on the ways that institutional characteristics, policies, and practices may mitigate the barriers that limit attainment of African American women in STEM fields. Drawing on the findings, the paper concludes with recommendations for improving policy and practice as well as recommendations for additional fruitful research.     

“In A Position I See Myself in:” (Re)Positioning Identities and Culturally-Responsive Pedagogies

Culturally-responsive pedagogies require moving beyond blanket assumptions about learners to focus deeply on local meaning-makings. This narrative analysis case study examines the ways a 20-year-old African American man challenges the negative educational identity with which he is forced to contend as he navigates a large and complex urban public school system. The ways in which Jamahl, a seeker of a High School Equivalency, refuses interpellation as an uneducated learner destined to be “nothin'” provides insight as to how formal education might be more responsive to learners' negotiation of deficiency discourses. Embracing agency, specifically through awareness of the ways Jamahl employs (re)positioning practices, this narrative analysis case study highlights paths for researchers and practitioners to tap into learners' resources to recognize and foster powerful learning identities.     

Efficacy beliefs,job satisfaction,stress and their influence on the occupational commitment of English-medium content teachers in the Dominican Republic

The purpose of the present study was to investigate the effects of efficacy, job satisfaction, job stress and their influence on the occupational commitment of English-medium content teachers. A total of 109 practicing English-medium and Spanish-medium content teachers from the Dominican Republic volunteered to participate in the study. Findings suggest both teacher self-efficacy and collective efficacy measures were inversely but significantly correlated with occupational commitment and positively correlated with job satisfaction. The results also showed significant differences between English-medium and Spanish-medium content teachers’ self-efficacy, job satisfaction, job stress and occupational commitment. These findings have significant implications for advancing theory and practice. Recommendations for further research are discussed.     

Multi-label Arabic text categorization: A benchmark and baseline comparison of multi-label learning algorithms

Multi-label text categorization refers to the problem of assigning each document to a subset of categories by means of multi-label learning algorithms. Unlike English and most other languages, the unavailability of Arabic benchmark datasets prevents evaluating multi-label learning algorithms for Arabic text categorization. As a result, only a few recent studies have dealt with multi-label Arabic text categorization on non-benchmark and inaccessible datasets. Therefore, this work aims to promote multi-label Arabic text categorization through (a) introducing “RTAnews”, a new benchmark dataset of multi-label Arabic news articles for text categorization and other supervised learning tasks. The benchmark is publicly available in several formats compatible with the existing multi-label learning tools, such as MEKA and Mulan. (b) Conducting an extensive comparison of most of the well-known multi-label learning algorithms for Arabic text categorization in order to have baseline results and show the effectiveness of these algorithms for Arabic text categorization on RTAnews. The evaluation involves four multi-label transformation-based algorithms: Binary Relevance, Classifier Chains, Calibrated Ranking by Pairwise Comparison and Label Powerset, with three base learners (Support Vector Machine, k-Nearest-Neighbors and Random Forest); and four adaptation-based algorithms (Multi-label kNN, Instance-Based Learning by Logistic Regression Multi-label, Binary Relevance kNN and RFBoost). The reported baseline results show that both RFBoost and Label Powerset with Support Vector Machine as base learner outperformed other compared algorithms. Results also demonstrated that adaptation-based algorithms are faster than transformation-based algorithms.