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The impact of the COVID-19 pandemic on crime has been highly variable. One possible source of variation runs indirectly through the impact that the pandemic had on groups tasked with preventing and responding to crime. Here, this paper aims to examine the impact of the pandemic on the activities undertaken by front-line workers in the City of Los Angeles Mayor’s Office of Gang Reduction and Youth Development (GRYD).

The authors use both autoregressive integrated moving average modeling and a regression-based event study design to identify changes in GRYD Community Intervention Worker proactive peacemaking and violence interruption activities induced by the onset of the City of Los Angeles “safter-at-home” lockdown.

Analyses show that the proactive peacemaking and violence interruption activities either remained stable or increased with the onset of the lockdown.

While the City of Los Angeles exempted GRYD’s Community Intervention Workers from lockdown restrictions, there was no guarantee that proactive peacemaking and violence interruption activities would continue unchanged. The authors conclude that these vital functions were indeed resilient in the face of major disruptions to daily life presented by the pandemic. However, the causal connection between stability in Community Intervention Worker activities and gang-related crime remains to be evaluated.

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 explore a national anti-hate messaging project, #USvsHate, and its call to students to create public messages refusing “hate, bias, and injustice.” Participants indicated that #USvsHate’s invitation to publicly express students’ ideas about equal human value functioned as a next step in furthering youth voice and critical consciousness toward societal inclusion and justice.

Using grounded theory, analysis drew from teacher interviews (n = 45), student focus groups (n = 30), anonymous participant questionnaires and student-created messages and backstories (n = 250) gathered between 2017 and 2020.

Participants indicated #USvsHate’s call to amplify student voice offered a next step to act upon awareness of social issues by denouncing hate while promoting inclusivity. Four invitations related to the project’s “anti-hate message” call emerged as important to participants: the invitation to comment personally on improving society; the creative invitation to share perspectives in any media form; the invitation to speak to a promised public audience; and the invitation to join a collective “us” improving society.

Youth voice and critical consciousness scholarship show the importance of supporting K12 youth to develop abilities to speak about injustice while pursuing an inclusive democracy. Still, less research highlights youth who might enter a classroom with some level of such awareness. This research extends existing scholarship by examining a potential next step to inviting critical consciousness and youth voice in any classroom. It also explores the potential pitfalls of this open-ended approach.

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.

This chapter explores the evolution of smart city thinking in order to have a clear understanding of what is involved in effectively and sustainably implementing a Smart City 4.0 strategy. The chapter illustrates that the concept of smart cities has evolved from the technology driven implementations to Cities as Open Innovation Platforms. These open and participatory platforms facilitate the interaction and collaboration of the city's citizens, government, industry, entrepreneurs, academia, creatives and the social sector so that they can harness their collective intelligence for innovation, experimentation and implementation of solutions that creates real transformational value for the betterment of the city's and its stakeholders. The author also identifies the key dimensions on which a smart city 4.0 concept must be built upon but highlights that depending on the composition of its stakeholder ecosystem, the City will prioritise different dimensions and so each smart city is unique. The chapter builds upon the experience of implementing a Smart City 4.0 project in Waterford, Ireland. Details of the smart city initiatives implemented are illustrates with examples.

The authors extend the World Bank's Logistics Performance Index (LPI) for 30 additional countries and 13 additional years. The authors develop an inexpensive method for extending survey data when frequent, universal surveys are unavailable. The authors identify groups of country characteristics that influence LPI scores.

Using data from the World Development Indicators—the broadest global dataset of country socioeconomic features—the authors test machine learning algorithms for their ability to predict the LPI. The authors examine importance scores to identify factors that influence LPI scores.

The best performing algorithm produces predictions on unseen data that account for nearly 90% of observed variation, and are accurate to within 6%. It performs twice as well as an OLS model with per capita income as the only predictor. Explanatory factors are business environment, economic structure, finance, environment, human development, and institutional quality.

Machine learning offers a simple, inexpensive way of extending the coverage of survey data. This dataset provides a richer picture of logistics performance around the world. The factors the authors identify as predicting higher LPI scores can help policymakers and practitioners target interventions.

This paper is one of the first applications of machine learning to extend coverage of an index based on an international survey. The authors use the new data to provide the most wide-ranging analysis of logistics performance across countries and over time. The output is an important resource for policymakers tracking performance, and researchers particularly in smaller and lower income countries. The authors also examine a wider range of explanatory factors for LPI scores than previous work.

This article examines the Library of Congress Subject Headings (LCSH), which is the most used subject heading system in the world and an instance of a controlled vocabulary (CV).

The method used to examine the system is based on both authors’ subject knowledge in the field of information science (IS) and the subfield of knowledge organization (KO). Core concepts in this domain were examined (1) by checking if they are present or not in the system; (2) if not, by determining whether LCSH contains alternative terms useful for searching documents about the missing concept, by examining books indexed by the Library of Congress; (3) by identifying the semantic relations between subject headings.

The results demonstrate fundamental problems in the logical consistency of the representation of IS and KO in LCSH.

The implications for CVs in general are discussed.

No previous study has used our method to examine LCSH’s coverage of IS.

A lot has been discussed about Peter Drucker, and there exists significant written content admiring or criticizing his work as a management writer. This paper aims to offer a holistic analysis of Peter Drucker’s written contributions to better understand his views of society, government and organizations of all kinds.

Many have written about Peter Drucker and his considerable impact on the practical and philosophical foundations of modern management. Yet, there has been no systematic scholarly evaluation of Drucker as a writer, although many have praised and criticized his written work on management. In this study, the authors offer an analysis of Peter Drucker’s written contributions to evaluate his central contributions, as well as how he communicated his ideas on society and management.

A comprehensive analysis of Drucker’s word usage and writing style throughout his writing career forms an evidence-based approach to better understand his viewpoints and objectively evaluate the criticisms surrounding his work.

This research contributes to a better understanding of Peter Drucker’s central contributions, concerns and sentiments, as it relates to not only business management but also to his views of society, government and organizations of all kinds. A reconsideration of Drucker as a writer presents possible implications for the practice of management.