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Police procedural justice is essential in shaping police legitimacy and public willingness to cooperate, yet factors that affect police fair treatment of citizens are not fully understood. Using the data of the National Police Research Platform (NPRP), Phase II, this study examines the effects of three key organizational factors (i.e. effective leadership, supervisory justice and department process fairness) on officers’ procedural justice in police stops.

Innovatively, this study links police data with citizens’ data and conducts multilevel analyses on the effects of a host of citizen, officer, incident, and, importantly, agency characteristics on officer behaviors during over 5,000 police stops nested within 48 police agencies.

The results showed that the fairness of the departmental process had a positive effect on officer procedural justice, while the fairness of the supervisor was inversely associated with procedural justice on the street.

The linked data demonstrated that organizational fairness affected street procedure justice.

Addresses the standardization of the measurements and the labels for concepts commonly used in the study of work organizations. As a reference handbook and research tool, seeks to improve measurement in the study of work organizations and to facilitate the teaching of introductory courses in this subject. Focuses solely on work organizations, that is, social systems in which members work for money. Defines measurement and distinguishes four levels: nominal, ordinal, interval and ratio. Selects specific measures on the basis of quality, diversity, simplicity and availability and evaluates each measure for its validity and reliability. Employs a set of 38 concepts ‐ ranging from “absenteeism” to “turnover” as the handbook’s frame of reference. Concludes by reviewing organizational measurement over the past 30 years and recommending future measurement reseach.

This study aims to explore the perception of algorithm accuracy among data professionals in higher education.

Social justice theory guided the qualitative descriptive study and emphasized four principles: access, participation, equity and human rights. Data collection included eight online open-ended questionnaires and six semi-structured interviews. Participants included higher education professionals who have worked with predictive algorithm (PA) recommendations programmed with student data.

Participants are aware of systemic and racial bias in their PA inputs and outputs and acknowledge their responsibility to ethically use PA recommendations with students in historically underrepresented groups (HUGs). For some participants, examining these topics through the lens of social justice was a new experience, which caused them to look at PAs in new ways.

Small sample size is a limitation of the study. Implications for practice include increased stakeholder training, creating an ethical data strategy that protects students, incorporating adverse childhood experiences data with algorithm recommendations, and applying a modified critical race theory framework to algorithm outputs.

The study explored the perception of algorithm accuracy among data professionals in higher education. Examining this topic through a social justice lens contributes to limited research in the field. It also presents implications for addressing racial bias when using PAs with students in HUGs.

Recent federal legislation and numerous public policy debates have relied heavily on estimates of the number of police agencies and police officers in the USA. Historically, these estimates have been problematic, varying tremendously over time, across different sources, and using different methodologies. Currently, the two main sources of agency‐level data for estimating these numbers are the FBI’s Uniform Crime Reports and the Census Bureau’s Law Enforcement Directory Survey. While there is a great deal of overlap between these two databases, each contains thousands of departments not listed in the other. Also, among those departments listed in one or more of these databases, there is tremendous variation in the number of police officers recorded. While some of the disparity can be explained by banal differences in counting and record‐keeping methods, much is rooted in differing definitions of what constitutes a “police officer” and a “police agency”. In this study, we closely examine both databases in an effort to account for the differences between them. In addition, we introduce a new data source derived from the records of the Justice Department’s Office of Community Oriented Policing Services (COPS). Based on a thorough exploration of all three databases, we discuss the impact of their differences on criminal justice policy and police research. We first develop our own estimate of the number of police departments and police officers in the USA that differs substantially from other current estimates. We then estimate the number of police officers that the COPS office and future evaluators should use as the baseline for measuring the Clinton Administration’s success at adding 100,000 officers to the streets of America. Finally, we offer a modest set of recommendations for achieving greater uniformity across separate police agency databases.

Data storytelling courses position students as agents in creating stories interpreted from data about a social problem or social justice issue. The purpose of this study is to explore two research questions: What themes characterized students’ iterative development of data story topics? Looking back at six years of iterative feedback, what categories of data literacy pedagogy did instructors engage for these themes?.

This project examines six years of data storytelling final projects using thematic analysis and three years of instructor feedback. Ten themes in final projects align with patterns in feedback. Reflections on pedagogical approaches to students’ topic development suggest extending data literacy pedagogy categories – formal, personal and folk (Pangrazio and Sefton-Green, 2020).

Data storytelling can develop students’ abilities to move from being consumers to creators of data and interpretations. The specific topic of personal data exposure or risk has presented some challenges for data literacy instruction (Bowler et al., 2017). What “personal” means in terms of data should be defined more broadly. Extending the data literacy pedagogy categories of formal, personal and folk (Pangrazio and Sefton-Green, 2020) could more effectively center social justice in data literacy instruction.

Implications for practice include positioning students as producers of data interpretation, such as role-playing data analysis or decision-making scenarios.

Data storytelling has the potential to address current challenges in data literacy pedagogy and in teaching critical data literacy.

Course descriptions provide a template for future data literacy pedagogy involving data storytelling, and findings suggest implications for expanding definitions and applications of personal and folk data literacies.

This paper aims to determine any barriers and gaps within one community Forensic Child and Adolescent Mental Health Service (FCAMHS) provision for young people presenting with risk of harm to others or involvement with the youth justice system, from Black, Asian and Minority Ethnic (BAME) backgrounds.

This study presents a systematic review of literature into the barriers and gaps within community FCAMHS, which showed little research in the area. Because of this, referral numbers for five major cities within the Yorkshire and Humber community FCAMHS region were compared to population statistics and youth justice caution and sentencing data.

Comparison suggests that BAME young people are under-represented across referrals to community FCAMHS. Also, there were differences in the representation of BAME young people when compared to youth justice data across the five major cities.

The lack of literature, combined with disparities in proportional representation of BAME young people within the Yorkshire and Humber FCAMHS, shows the need for a detailed study and analysis within national community FCAMHS.

More needs to be done within community FCAMHS to ensure that services are meeting the needs of young people at risk of criminalisation.

To the best of the author’s knowledge, there is currently no published literature that explores ethnic representation and access to community-based FCAMH services. Exploration within one regional service suggests that young people from BAME backgrounds may be under-represented within such services, whilst at the same time being over-represented within formal criminal justice.

The purpose of this paper is to test Mastrofski's six‐dimensional conceptualization of perceived service quality of the police.

The paper draws on data collected from a mail survey of residents in a suburban Virginia (USA) community and uses confirmatory factor analysis to test the six‐dimensional model of service quality.

The six‐dimensional model does not fit the data. Instead a one‐factor model fits the data, suggesting that public perceptions of police service quality are a one‐dimensional construct.

Further research is necessary on two fronts. First, research should seek to determine whether the findings are consistent across different community contexts and types of samples. Second, research on related perceptual constructs, including procedural justice and legitimacy, should explore measurement properties in more detail.

This is one of few studies to examine the construct validity of public perception measures in police research.

The purpose of this paper is to analyse data literacy in the new Digital Competence Framework for Citizens (DigComp 2.2). Mid-2022 the Joint Research Centre of the European Commission published a new version of the DigComp (EC, 2022). This new version focusses more on the datafication of society and emerging technologies, such as artificial intelligence. This paper analyses how DigComp 2.2 defines data literacy and how the framework looks at this from a societal lens.

This study critically examines DigComp 2.2, using the data literacy competence model developed by the Knowledge Centre for Digital and Media Literacy Flanders-Belgium. The examples of knowledge, skills and attitudes focussing on data literacy (n = 84) are coded and mapped onto the data literacy competence model, which differentiates between using data and understanding data.

Data literacy is well-covered in the framework, but there is a stronger emphasis on understanding data rather than using data, for example, collecting data is only coded once. Thematically, DigComp 2.2 primarily focusses on security and privacy (31 codes), with less attention given to the societal impact of data, such as environmental impact or data fairness.

Given the datafication of society, data literacy has become increasingly important. DigComp is widely used across different disciplines and now integrates data literacy as a required competence for citizens. It is, thus, relevant to analyse its views on data literacy and emerging technologies, as it will have a strong impact on education in Europe.

The nature and extent of adults' engagement in diverse manifestations of technology-facilitated aggression is not yet well understood. Most research has focused on victimization. When explored, engagement in online aggression and abuse has centered on children and young people, particularly in school and higher education settings. Drawing on nationally representative data from New Zealand adults aged 18 and over, this chapter explores the overall prevalence of online aggression with a focus on gender and age. Our findings support the need to also understand adult aggressors' behaviors to better address the distress and harm caused to targets through digital communications. The chapter discusses the implications of the results for policy and practice and proposes some directions for future research.