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We need a locally relevant curriculum because it is engaging and leverages community knowledge strengths. However, new teachers are not always aware of the resources available to make a locally relevant curriculum. Here in New Mexico, Los Alamos is a location with many resources detailing its purpose and existence. These resources coupled with so much notoriety inside and outside the state make Los Alamos a place that lends itself to culturally relevant instruction. Specifically, graphic novels provide a unique medium for students and teachers alike to start learning about the city that started the Atomic Age: Los Alamos and begin applying that knowledge more broadly. I, being a student and a teacher from New Mexico, offer my own understanding of a locally relevant curriculum utilizing three graphic novels about Los Alamos, its people, and its stories.

This chapter reviews graphical modeling techniques for estimating large covariance matrices and their inverse. The chapter provides a selective survey of different models and estimators proposed by the graphical modeling literature and offers some practical examples where these methods could be applied in the area of health economics.

The latest financial crisis has stressed the need of understanding the world financial system as a network of interconnected institutions, where financial linkages play a fundamental role in the spread of systemic risks. In this paper we propose to enrich the topological perspective of network models with a more structured statistical framework, that of Bayesian Gaussian graphical models. From a statistical viewpoint, we propose a new class of hierarchical Bayesian graphical models that can split correlations between institutions into country specific and idiosyncratic ones, in a way that parallels the decomposition of returns in the well-known Capital Asset Pricing Model. From a financial economics viewpoint, we suggest a way to model systemic risk that can explicitly take into account frictions between different financial markets, particularly suited to study the ongoing banking union process in Europe. From a computational viewpoint, we develop a novel Markov chain Monte Carlo algorithm based on Bayes factor thresholding.

Purpose: The public health burden caused by tobacco is heavy among first-generation Chinese immigrant men whose home country has significantly higher smoking rates than the United States. The current study is part of a larger effort to pilot an mHealth tobacco cessation intervention using MMS (graphic) mobile phone technologies to target East Asian immigrant populations. Grounded in the Extended Parallel Process Model (EPPM), our specific aims were to determine what message themes, level of graphic intensity, and types of efficacy information are most appropriate and useful for mHealth interventions targeting this population.

Methodology/Approach: A qualitative study utilizing a series of focus groups (k = 5) was conducted with male adult smokers who were born in China and currently reside in the United States. The primary aim of the focus groups was to solicit reactions to a series of preliminary messages developed by the research team. A secondary aim was to gauge receptivity to the use of MMS as a vehicle for smoking cessation intervention. Participants (n = 32) were recruited from local Chinese communities in a large Mid-Atlantic metropolitan area.

Findings: Opinions about different message strategies were mixed. However, participants tended to rate messages more positively when they focused on the impact of smoking on family and loved ones, particularly children. Messages with fear-arousing images were also perceived to be effective at low frequency of exposure, but there were concerns that they may backfire at high exposure. Awareness of and interest in Quitline were low, and concrete quitting tips were perceived as more effective. Participants reported a preference for receiving messages a few times a week, and an MMS message platform was generally preferred to WeChat (a Chinese social media platform).

Implications: Our results suggest that graphic MMS messaging holds promise as an effective intervention method for this population and that EPPM is an appropriate framework to develop, test, and analyze mHealth intervention messages. While messages that focused primarily on impact on children, health, and specific quitting tips were generally found to be more effective, a mix of different types of messages that address a wide range of issues may be most appropriate for this population.

Originality/Value: This study is the first to explore the utility of graphic text messaging as an intervention method to promote smoking cessation among male Chinese immigrants. Findings from the study provide important insights for future intervention work targeting this underserved population.

Comics and graphic novels can disrupt traditional texts by challenging the “worship of the written word” (Torres, 2019), a feature of white supremacy that perpetuates textual hierarchies within educational spaces. Giving all of our students access to contemporary literature that centers Black youth perspectives is not only important in decolonizing literature education but also in presenting a holistic view of Black childhood. They can be used in the classroom as subjects to challenge stereotypical depictions by centering experiences, ideas, and concepts that are often marginalized in traditional curriculum. Within this chapter, we focus on comics and graphic novels as tools to enact students’ multiliteracies and to analyze visual stories depicting BlackBoy adolescence, using the frameworks of BlackBoy Crit Pedagogy (Bryan, 2022), an equity framework that interrogates the interdisciplinary ways that Black boy students' literacy learning can be formed through the teaching and learning of Blackness, maleness, and the schooling experiences of Black boys. We utilize this framework to analyze the use of diverse comics and graphic novels to facilitate critical conversations of bringing inclusive visual texts into the classroom. We invite practitioners to reimagine curricular ideas and content centered on empowerment and Black boy adolescence and how those ideas are presented to youth through a variety of visual narratives.

Data visualizations in some form or another have served as decision-support tools for many centuries. In conjunction with advancements in information technology, data visualizations have become more accessible and more efficient to generate. In fact, virtually all enterprise resource planning and human resource (HR) information system vendors offer off-the-shelf data visualizations as part of decision-support dashboards as well as stand-alone images and displays for reporting. Plus, advances in programing languages and software such as Tableau, Microsoft Power BI, R, and Python have expanded the possibilities of fully customized graphics. Despite the proliferation of data visualization, relatively little is known about how to design data visualizations for displaying different types of HR data to different user groups, for different purposes, and with the overarching goal of improving the ways in which users comprehend and interpret data visualizations for decision-making purposes. To understand the state of science and practice as they relate to HR data visualizations and data visualizations in general, we review the literature on data visualizations across disciplines and offer an organizing framework that emphasizes the roles data visualization characteristics (e.g., display type, features), user characteristics (e.g., experience, individual differences), tasks, and objectives (e.g., compare values) play in user comprehension, interpretation, and decision-making. Finally, we close by proposing future directions for science and practice.

Purpose – Crime, criminals, the criminal justice system, and criminal justice system actors have traditionally occupied a prominent place in popular media. Comic books and graphic novels are no exception to this trend. Despite this, these media have received comparatively little attention from criminal justice scholars. This chapter seeks to explore the depiction of crime and justice in modern-era comic books and graphic novels.

Methodology/approach – Content analysis techniques were used to examine 166 individual comic books from the modern age (mid-1980s to present), including those compiled in graphic novel form. Particular emphasis was placed on issues of crime control and due process.

Findings – Clear criminal justice themes were seen across the sample, including an emphasis on crime control and crime prevention. Further, comic books featuring the individual characters of Superman and Batman portrayed opposing conceptions of justice, such as justified/unjustified use of force and a willingness to follow or break the law.

Research limitations – This research represents an exploration of the depiction of crime-related themes in comic books and graphic novels, but is by no means definitive. It would be useful to extend this research by examining other eras in comic book history as well as other comic book characters and publishing companies.

Practical implications – The public's perceptions of the criminal justice system ultimately affect societal views of the legitimacy of the system. Since legitimacy is a requisite for compliance, it is important to understand factors that may influence these perceptions. These may include comic books, graphic novels, and other popular media.

Originality/value of paper – Comic books stories and themes have long reflected the times. However, it is unclear how crime and the criminal justice system are portrayed in the comic book world. This chapter is an attempt to fill a gap in the extant literature by examining this often neglected form of popular media.

The technique of graphical modelling (Whittaker, 1990) can be used to identify the dependence relationships between variables representing characteristics of recorded road accidents. It allows large multi-dimensional tables to be analysed by looking for conditional independence relationships among the variables. The variables under study can often be divided into groups that are ordered in time or by a hypothesised causal assumption. For these situations graphical chain models (Whittaker, 1990) are used to explore causal relationships between the variables. Some examples are given for a six-dimensional and a ten-dimensional contingency table.