Purpose: The Theory Evaluation Scale (TES) is a psychometric tool for analyzing theoretical frameworks that guide practice. Its flexibility assumption contends that a single rater or a panel of experts can evaluate a given theory and the two sets of scores will be comparable. This study tests this assumption, using a popular practice model in social work: the Strengths Perspective (SP). Method: A panel of 50 social work faculty and administrators from five English-speaking countries used the TES to determine the theoretical quality of the SP. Their scores were first analyzed through common measures of central tendency and then compared to findings in an existing evaluation for the same theory. Results: The analysis yielded excellent overall TES scores for the SP (mean = 32.03, median = 33.00, mode = 36). These results reflect the 35 overall score that the theory received in Joseph et al.'s (2022) evaluation. Conclusion: Therefore, besides showing an excellent overall quality for the SP, these results support the flexibility assumption of the TES. These findings bear major implications for social work theory, practice, and research.

Generalizing is a critical aspect of mathematics learning, with researchers and policy documents highlighting generalizing as a core mathematical practice. It can also be challenging to foster in class settings, and teachers need access to better resources to teach generalizing, including an understanding of effective forms of instruction. This article proposes Classroom Supports for Generalizing (CSGs), investigating how multiple elements—such as tasks, teacher moves, student interactions, and representations—interact to meaningfully foster student generalizing. Drawing on class video data from a middle school teacher and two high school teachers, we present the CSG Framework, which identifies three categories of supports: Interactions for Generalizing, Structures for Generalizing, and Routines for Generalizing.

In this paper, we investigate ambiguity in language use that is intended by the speaker. Our overall argument is that, as a general feature of language resulting from the indeterminacy of meaning at all levels of language, ambiguity can be taken advantage of in specific contexts for a host of purposes. Given that an ambiguous utterance has (at least) two possible meanings – A and B – there are two logical possibilities for a speaker in a deliberate ambiguity: to mean both A and B or to mean only A (or B). However, a closer examination of the literature and available data reveal a far more complicated picture: there are more strategies under either of these possibilities, each of which closely interacts with the context and is motivated by the speaker's purpose of communication in that context. The treatment of deliberate ambiguity in the present study is thus more coherent theoretically and more comprehensive in coverage than what is found in the sizable literature on the subject.

Not all recoveries are successful. Previous research mostly focuses on employee service-recovery actions, while limited research examines how customers evaluate the interactions of employees’ diverse appearance features and their performance in service recoveries. To build upon the research gaps, this study examines the two processes of evaluation: inference-based and recognition-based evaluations. To do so, this research examines how service employees’ diversity features (e.g., gender and race) and their organizational citizenship behavior (OCB) performance influence customer evaluations of service recovery. Using a between-subjects experimental research design, the findings contribute to the diversity, service recovery, and OCB literature by adding new evidence on how recognition-based evaluations of employees’ appearances (gender and race) and inference-based evaluations of behaviors (OCB to customers) can affect customer perceptions of employee competence, trustworthiness, and dignity in service recoveries.

Abstract Why do scholar–teachers use simulations? This study presents an analysis of simulation goals in peer-reviewed simulations published over the past 20 years. The scholarship of teaching and learning emphasizes that educational goals are an essential first step in designing simulations. The flexibility of simulation designs allows the formulation of a variety of content-based, skill-based, and emotion-based goals. However, which of these goals are more prevalent in published simulations of political science and international relations? Is there a change in the stated simulation objectives over time? And do scholar–teachers outline simulation goals in the first place? This study answers these questions by collecting and analyzing data on the stated simulation objectives in a random sample of eighty-seven simulations published in the Journal of Political Science Education (2005–2021), International Studies Perspectives (2000–2021), and PS: Political Science and Politics (2000–2021). The key finding is that the simulation community is doing well and, over time, is making progress in outlining and diversifying the intended learning outcomes of simulations. What is still needed, however, is a greater recognition of the emotional impacts of simulations and their potential to advance not only student substantive knowledge but also their skills and emotional experiences, with all three in the same simulation design.

ABSTRACT This study explores the impacts of the referrer’s risk preference and reward size on the number of referrals and type of friends referred (i.e., tie strength). Risk preference (i.e., appetite for risk) is found to be related positively to the number of referrals and the percentage of weak ties reached, regardless of the reward. The effect differences between risk preference groups are greater if a medium-sized reward is offered, however, mainly due to the greater concavity exhibited by people with lower risk preference in their utility function.

Deep learning (DL) can provide critical infrastructure operators with valuable insights and predictive capabilities to help them make more informed decisions, improving system's robustness. However, training DL models requires large amounts of data, which can be costly to store in a centralized manner. Storing large amounts of sensitive critical infrastructure data in the cloud can pose significant security risks. Federated learning (FL) allows several clients to share learning data and train ML models. Unlike centralized models, FL does not require the sharing of client data. A novel framework is presented to train a VGG16 based CNN global model without sharing the data and only updating the local models among clients using federated averaging. For experimentation, MNIST dataset is used. The framework achieves high accuracy and keep data private using FL in critical infrastructures. The benefits and challenges of FL along with security vulnerabilities and attacks have been discussed along with the defenses that can be used to mitigate these attacks.

A long-standing debate among cognitive scientists has focused on describing the underlying nature of executive functions, which has important implications for both theoretical and applied research. Miyake et al.'s three-factor model has often been considered the gold-standard representation of executive functions and has driven much research in the field. More recently, however, there have been increasing concerns that the three-factor model does not adequately describe a highly complex construct such as executive functions. The current project presents two studies that examine the veracity of Miyake et al.'s model and propose a new approach (i.e., network modeling) for detecting the underlying nature of executive functions. The current results raise questions about the psychometric strength and adequacy of the three-factor model. Further, the studies presented here provide evidence that network modeling provides a better understanding of executive functions as it better captures (relative to latent variable modeling) the complexity of cognitive processes. Theoretical and applied implications are discussed.

In this contribution, we examine a virtual, cross-campus collaboration during the fall of 2020. Our goal was for students to develop deeper knowledge of the materiality of borders through course content as well as getting to know each other and their different contexts in iterative ways. We approached this goal by intentionally seeking to engage our students, who were facing virtual classes for the second or third time given the Covid-19 pandemic. We knew students struggled with online platforms in previous terms and aimed to provide a classroom experience that recognized the limits of screen-based interaction and the multitude of precarities accentuated by the pandemic. While attending our classes, students were also facing the interlocking effects of Covid-19 including illness and death in their families, effects on their employment and mobility, and associated mental health struggles. Thus, community building within and across these classes was key as we navigated teaching online. Finally, we address our social location regarding course content given our complicated histories of migration and identities as undocuscholars: growing up and attending higher education undocumented in the U.S. (though no longer undocumented).