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Homelessness has many causes and also is stigmatized in the United States, leading to much misunderstanding of its causes and what policy solutions may ameliorate the problem. The problem is of course getting worse and impacting many communities far removed from the West Coast cities the authors examine in this study. This analysis examines the socioeconomic variables influencing homelessness on the West Coast in recent years. The authors utilize a panel fixed effects model that explicitly includes measures of healthcare access and availability to account for the additional health risks faced by individuals who lack shelter. The authors estimate a spatial error model (SEM) in order to better understand the impacts that systemic shocks, such as the COVID-19 pandemic, have on a variety of factors that directly influence productivity and other measures of welfare such as income inequality, housing supply, healthcare investment, and homelessness.

The purpose of this article is to share this study’s efforts to foster belonging in special collections public service spaces, as Black and Latina practitioners of color, while navigating known systemic professional barriers to inclusivity in the library information profession.

In this conceptual essay, frameworks from Black and Chicana feminist theories are applied which resonate deeply with this study’s practices but are not often encountered in library spaces, namely intersectional nepantla, which is used to situate the positionality within special collections.

Fostering belonging in special collections environments is an ongoing effort, but this study offers reflections in solidarity with all who seek to increase inclusivity and equity in their spaces. It is believed that the cumulative impact of many small actions implemented from the ground up can potentially be as significant as top-down, administrative charges.

This article's originality stems from both its authors and the methodology. As BIPOC practitioners, to the authors emphasize the authentic, day-to-day interactions that are essential to developing inclusivity and equity in special collections and archival spaces. Special collections reference workers have limited time off desk to collaborate and conduct research.

This paper aims to center the experiences of three cohorts (n = 40) of Black high school students who participated in a critical race technology course that exposed anti-blackness as the organizing logic and default setting of digital and artificially intelligent technology. This paper centers the voices, experiences and technological innovations of the students, and in doing so, introduces a new type of digital literacy: critical race algorithmic literacy.

Data for this study include student interviews (called “talk backs”), journal reflections and final technology presentations.

Broadly, the data suggests that critical race algorithmic literacies prepare Black students to critically read the algorithmic word (e.g. data, code, machine learning models, etc.) so that they can not only resist and survive, but also rebuild and reimagine the algorithmic world.

While critical race media literacy draws upon critical race theory in education – a theorization of race, and a critique of white supremacy and multiculturalism in schools – critical race algorithmic literacy is rooted in critical race technology theory, which is a theorization of blackness as a technology and a critique of algorithmic anti-blackness as the organizing logic of schools and AI systems.

Given the historical legacy of policing Black bodies, this research focuses on the structures of anti-Blackness within school policing and the strategies students of Color activists use as they work to defund or abolish police departments in the Los Angeles Unified School District (LAUSD).

Specifically, this article looks to Twitter as a counter-storytelling space for students of Color activists to organize and build movements to end anti-Black school policing. Through the frameworks of critical race theory (CRT) and Black critical theory (BlackCrit), this research applies inductive coding to analyze 42 Twitter posts from three students of Color-led organizations based in Los Angeles.

This document analysis presents four themes, which describe four dominant strategies students of Color activists use in their campaigns to defund or abolish school police in the LAUSD: (1) centering Blackness and Black student experiences, (2) making demands for the elimination of funding and support for school police, (3) calling for a shift in funding to support Black students and (4) employing multiple tactics concurrently.

These findings demonstrate the importance of developing and centering a critical understanding of anti-Blackness to achieve racial and educational justice within social movements.

Moreover, the demands of students of Color activists reflect visions of public schools free from anti-Black school policing.

In this chapter, the authors envision a new framework for technology-enabled local engagement. This framework would exploit web-based collaboration technology in order to create local engagement panels that represent a cross section of ordinary residents. For concreteness, the authors illustrate the framework in a proposed study called “Clearing the Air” would enable residents in the areas of Southern California that are most impacted by the logistics industry to have substantive and constructive opportunities to engage local officials on the complex economic and environmental matters related to logistics and emissions in the Southern California region. The authors propose methods to evaluate best practices in the use of collaboration technology, in particular, to learn whether the technology enables and empowers the engagement panels to participate in air quality governance at a high level and helps local officials better understand the considered opinions of residents on these important matters. If successful, this framework would integrate the policy views of individual residents alongside those of organized stakeholders, experts, and agency officials in the policy process.

In the state of Hawaii, it has been shown that certain ethnic minority groups, such as Filipinos and Pacific Islanders, suffer disproportionally high rates of cardiovascular disease, evidence that local health-care systems and governing bodies fail to equally extend the human right to health to all. This study aims to examine whether these ethnic health disparities in cardiovascular disease persist even within an already globally disadvantaged group, the houseless population of Hawaii.

A retrospective chart review of records from Hawaii Houseless Outreach and Medical Education Project clinic sites from 2016 to 2020 was performed to gather patient demographics and reported histories of type II diabetes, obesity, hyperlipidemia, hypertension and other cardiovascular disease diagnoses. Reported disease prevalence rates were compared between larger ethnic categories as well as ethnic subgroups.

Unexpectedly, the data revealed lower reported prevalence rates of most cardiometabolic diseases among the houseless compared to the general population. However, multiple ethnic health disparities were identified, including higher rates of diabetes and obesity among Native Hawaiians and other Pacific Islanders and higher rates of hypertension among Filipinos and Asians overall. The findings suggest that even within a generally disadvantaged houseless population, disparities in health outcomes persist between ethnic groups and that ethnocultural considerations are just as important in caring for this vulnerable population.

To the best of the authors’ knowledge, this is the first comprehensive study focusing on ethnic health disparities in cardiovascular disease and the structural processes that contribute to them, among a houseless population in the ethnically diverse state of Hawaii.

This paper aims to analyze decisions about payment rates on credit card statements by using background factors and perceptions that indirectly influence beliefs, according to the theory of planned behavior.

Since legal and institutional frameworks and household financial surveys are heterogeneous among countries, household data on the Chilean economy is used as the starting point in this matter.

The probability that an individual chooses to pay amounts less than the total billing of their credit cards rises with essential variables related to perceived behavioral control. Being the head of the household, being younger, perceiving a high or excessive financial burden of debt and facing unfavorable and unexpected situations that divert the budget, among others, are relevant to repayment decisions.

The novelty of this article is that its psychological approach differs from the traditional focus of economic rationality regarding credit cards. The results are relevant for policymakers and financial regulators due to implications for household behavioral finance and means of payment.

Analizamos la decisión de la tasa de pago de los estados de cuenta de tarjetas de crédito a través del uso de factores de fondo y percepciones que indirectamente inciden en las creencias de acuerdo a la teoría del comportamiento planeado.

Debido a que los marcos legales e institucionales, así como también las encuestas financieras de hogares son heterogéneas entre países, se utilizan datos de los hogares de la economía chilena como un punto de partida en esta materia.

La probabilidad de que un individuo elija pagar un monto menor que el total de facturación de sus tarjetas de crédito es afectada por variables proxy asociadas al control conductual percibido. La condición de ser jefe de hogar, ser más joven, la percepción de una alta o excesiva carga financiera de la deuda, y enfrentar situaciones desfavorables e inesperadas que desvían del presupuesto, entre otras, son relevantes para las decisiones de pago.

La novedad de este artículo es que su enfoque difiere del enfoque tradicional de la racionalidad económica en relación a las tarjetas de crédito. Los resultados son relevantes para los hacedores de política y reguladores financieros debido a sus implicancias para las finanzas conductuales de los hogares y sus medios de pago.

This paper aims to endorse and elaborate on the recommendations put forward by the Sharland Foundation Developmental Disabilities Applied Behavioural Research and Impact Network (SF-DDARIN), emphasising their significance in the field of developmental disabilities.

This paper outlines a specific point of view. The first section focuses on integrating developmental theory and advanced technology in interventions for developmental disabilities. Subsequently, the commentary explores virtual reality (VR) and generative artificial intelligence (AI) for enhancing social skills and personalising support. Finally, the piece highlights innovations like SocialWise VR and Custom Generative Pre-Trained Transformers in aligning interventions with developmental stages.

Technologies like VR and generative AI hold vast potential to revolutionise how clinicians provide timely and relevant knowledge to individuals with developmental disabilities.

This is strictly a commentary.

Availability of technology.

Both VR and generative AI will impact service delivery in a meaningful way.

The paper advocates for incorporating these technologies into SF-DDARIN's approach, emphasising their potential to revolutionise evidence-based interventions in developmental disabilities.

Traffic flow prediction has always been a top priority of intelligent transportation systems. There are many mature methods for short-term traffic flow prediction. However, the existing methods are often insufficient in capturing long-term spatial-temporal dependencies. To predict long-term dependencies more accurately, in this paper, a new and more effective traffic flow prediction model is proposed.

This paper proposes a new and more effective traffic flow prediction model, named channel attention-based spatial-temporal graph neural networks. A graph convolutional network is used to extract local spatial-temporal correlations, a channel attention mechanism is used to enhance the influence of nearby spatial-temporal dependencies on decision-making and a transformer mechanism is used to capture long-term dependencies.

The proposed model is applied to two common highway datasets: METR-LA collected in Los Angeles and PEMS-BAY collected in the California Bay Area. This model outperforms the other five in terms of performance on three performance metrics a popular model.

(1) Based on the spatial-temporal synchronization graph convolution module, a spatial-temporal channel attention module is designed to increase the influence of proximity dependence on decision-making by enhancing or suppressing different channels. (2) To better capture long-term dependencies, the transformer module is introduced.