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The paper examines the historical shifts in policing strategies towards individuals with SMI and vulnerable populations, highlighting the development of co-response models, introducing the concept of “untethered” co-response.

This paper conducts a review of literature to trace the evolution of police responses to individuals with serious mental illness (SMI) and vulnerable populations. It categorizes four generations of police approaches—zero-policing, over-policing, crisis intervention and co-response—and introduces a fifth generation, the “untethered” co-response model exemplified by Project SCOPE in Philadelphia.

The review identifies historical patterns of police response to SMI individuals, emphasizing the challenges and consequences associated with over-policing. It outlines the evolution from crisis intervention teams to co-response models and introduces Project SCOPE as an innovative “untethered” co-response approach.

The research acknowledges the challenges in evaluating the effectiveness of crisis intervention teams and co-response models due to variations in implementation and limited standardized models. It emphasizes the need for more rigorous research, including randomized controlled trials, to substantiate claims about the effectiveness of these models.

The paper suggests that the “untethered” co-response model, exemplified by Project SCOPE, has the potential to positively impact criminal justice and social service outcomes for vulnerable populations. It encourages ongoing policy and evaluative research to inform evidence-based practice and mitigate collateral harms associated with policing responses.

Given the rising interactions between police and individuals with mental health issues, exacerbated by the COVID-19 pandemic, the paper highlights the urgency for innovative, non-policing-driven responses to vulnerable persons.

The paper contributes to the literature by proposing a fifth generation of police response to vulnerable persons, the “untethered” co-response model and presenting Project SCOPE as a practical example.

The purpose of this paper is to explore teacher candidates’ response to young adult literature (prose and comics) featuring fat identified protagonists. The paper considers the textual and embodied resources readers use and reject when imagining and interpreting a character’s body. This paper explores how readers’ meaning making was influenced when reading prose versus comics. This paper adds to a corpus of scholarship about the relationships between young adult literature, comics, bodies and reader response theory.

At the time of the study, participants were enrolled in a teacher education program at a Midwestern University, meeting monthly for a voluntary book club dedicated to reading and discussing young adult literature. To examine readers’ responses to comics and prose featuring fat-identified protagonists, the author used descriptive qualitative methodologies to conduct a thematic analysis of meeting transcripts, written participant reflections and researcher memos. Analysis was grounded in theories of reader response, critical fat studies and multimodality.

Analyses indicated many readers reject textual clues indicating a character’s body size and weight were different from their own. Readers read their bodies into the stories, regarding them as self-help narratives instead of radical counternarratives. Some readers were not able to read against their assumptions of thinness (and whiteness) until prompted by the researcher and other participants.

Although many reader response scholars have demonstrated readers’ tendencies toward personal identification in the face of racial and class differences, there is less research regarding classroom practices around the entanglement of physical bodies, body image and texts. Analyzing reader’s responses to the constructions of fat bodies in prose versus comics may help English Language Arts (ELA) educators and students identify and deconstruct ideologies of thin-thinking and fatphobia. This study, which demonstrates thin readers’ tendencies to overidentify with protagonists, suggests ELA classrooms might encourage readers to engage in critical literacies that support them in reading both with and against their identities.

Conflict and violence are the main drivers of globally escalating humanitarian needs. Local grassroots initiatives are pivotal in distributing humanitarian supplies in the acute response phase until more established humanitarian aid organizations can enter. Nevertheless, scant research exists regarding the role of grassroots associations in providing humanitarian assistance during a military conflict. The purpose of this paper is to understand the role of grassroots associations and identify important themes for effective operations.

This paper adopts a case-study approach of three Ukrainian grassroots associations that began operating in the immediate days of the full-scale invasion of Ukraine. The findings are based on analyzing primary sources, including interviews with Ukrainian volunteers, and are supported by secondary sources.

Grassroots associations have local contacts and a contextual understanding of population needs and can respond more rapidly and effectively than large intergovernmental agencies. Four critical themes regarding the operations of grassroots associations emerged: information management, inventory management, coordination and performance measurement. Grassroots humanitarian response operations during conflict are challenged by personal security risks, the unpredictability of unsolicited supplies, emerging volunteer roles, dynamic transportation routes and shifting demands.

Grassroots responses are central to humanitarian responses during the acute phase of a military conflict. By examining the operations of grassroots associations in the early months of the 2022 war in Ukraine, the authors provide a unique perspective on humanitarian logistics. Nonetheless, more inclusive models of humanitarian responses are needed to harness the capacities and resilience of grassroots operations in practice.

The objective of this research is to investigate the capabilities of the ChatGPT GPT-4 model, a form of artificial intelligence (AI), in comparison to human experts in the context of construction project risk management.

Employing a mixed-methods approach, the study draws a qualitative and quantitative comparison between 16 human risk management experts from Finnish construction companies and the ChatGPT AI model utilizing anonymous peer reviews. It focuses primarily on the areas of risk identification, analysis, and control.

ChatGPT has demonstrated a superior ability to generate comprehensive risk management plans, with its quantitative scores significantly surpassing the human average. Nonetheless, the AI model's strategies are found to lack practicality and specificity, areas where human expertise excels.

This study marks a significant advancement in construction project risk management research by conducting a pioneering blind-review study that assesses the capabilities of the advanced AI model, GPT-4, against those of human experts. Emphasizing the evolution from earlier GPT models, this research not only underscores the innovative application of ChatGPT-4 but also the critical role of anonymized peer evaluations in enhancing the objectivity of findings. It illuminates the synergistic potential of AI and human expertise, advocating for a collaborative model where AI serves as an augmentative tool, thereby optimizing human performance in identifying and managing risks.

This research paper aims to evaluate the sustainability knowledge and background of students, staff and faculty regarding current university sustainability practices and individual behaviors at Central Michigan University (CMU); to compare sustainability background and knowledge based on academic discipline of enrollment or employment; and to assess sustainability awareness and interest of the campus community to guide future sustainability initiatives and resources at CMU.

An electronic cross-sectional survey was used to collect anonymous responses through Qualtrics, and then results were analyzed through SPSS. Analyses were performed based on the academic structures at CMU.

This research has found that students in STEM fields are more inclined to have pro-sustainability attitudes, knowledge and behaviors, compared to those studying the arts and business. Additionally, results indicate that there is a significant difference in knowledge between the students, and the staff and faculty respondents regarding sustainability knowledge and application, with the staff and faculty consistently demonstrating more pro-sustainability knowledge and behavior.

While research has previously been conducted on sustainability attitudes and behaviors, this research is unique because it ties sustainability knowledge to academic discipline. Additionally, it serves to gauge which sustainability programs and topics members of the campus community are most interested in, and which areas they are most willing to support.

Engaging in webcare, i.e. responding to online reviews, can positively affect consumer attitudes, intentions and behavior. Research is often scarce or inconsistent regarding the effects of specific webcare strategies on business performance. Therefore, this study tests whether and how several webcare strategies affect hotel bookings.

We apply machine learning classifiers to secondary data (webcare messages) to classify webcare variables to be included in a regression analysis looking at the effect of these strategies on hotel bookings while controlling for possible confounds such as seasonality and hotel-specific effects.

The strategies that have a positive effect on bookings are directing reviewers to a private channel, being defensive, offering compensation and having managers sign the response. Webcare strategies to be avoided are apologies, merely asking for more information, inviting customers for another visit and adding informal non-verbal cues. Strategies that do not appear to affect future bookings are expressing gratitude, personalizing and having staff members (rather than managers) sign webcare.

These findings help managers optimize their webcare strategy for better business results and develop automated webcare.

We look into several commonly used and studied webcare strategies that affect actual business outcomes, being that most previous research studies are experimental or look into a very limited set of strategies.

National surveys reveal that sports fans exhibit greater support for athletes, sports teams and leagues endorsing social justice initiatives compared to the general population, highlighting the potential of sports for positive social impact. This study investigates whether such responses are influenced by systematic biases.

Replicating a Nielsen national survey, two experiments explore whether biases affect support for athletes' participation in the Black Lives Matter (BLM) movement. The study also examines partisan motivated reasoning as a factor driving sports fans' support for BLM.

While avid fans display stronger endorsement of BLM compared to causal/non-sports fans, evidence suggests that systematic biases distort these responses. When sport identity becomes salient, reported support for the BLM movement becomes inflated.

Researchers often employ self-report surveys to gauge audience perceptions of athlete activism or cause-related initiatives, particularly when assessing their impact. This study's findings indicate that this context is susceptible to SDB.

The study underscores the role of systematic biases in self-report surveys, particularly in socially desirable contexts. People tend to over-report “positive behavior,” leading survey participants to respond more favorably to questions that are socially desirable. Therefore, interpreting survey results with caution becomes essential when the research context is deemed socially (un)desirable. It is crucial for researchers to apply appropriate measures to identify and mitigate systematic response biases. The authors recommend that researchers adopt both procedural and statistical remedies to detect and reduce social desirability biases.

The purpose of this study is to investigate the phenomenon of defensive biasing in work stress surveys, which occurs when employees trivialize potential stressors and strains due to fear of negative consequences from their supervisors or management. This study aims to better understand the factors that influence this behavior and to develop a scale to measure it.

The study used an online survey of 200 employees to investigate the factors influencing defensive biasing behavior. The researchers developed a scale for defensive biasing with the help of subject matter experts and derived possible factors from the literature. Participants were presented with a hypothetical scenario in which they imagined a work stress survey in their organization and were asked to answer related items. The data were analyzed using regression analysis.

The study found that defensive biasing behavior was significantly predicted by perceived anonymity and neuroticism. Participants who felt less anonymous and had higher levels of neuroticism were more likely to engage in defensive biasing. Job insecurity and trust in supervisors were not found to be significant predictors of defensive biasing.

This study contributes to the literature on work stress surveys by developing a scale for defensive biasing and investigating the factors that influence this behavior. The study highlights the importance of making the survey process more transparent to reduce defensive biasing and obtain trustworthy results.

Pre-positioning and distributing relief items are important parts of disaster management as it simultaneously considers activities from both pre- and post-disaster stages. This study aims to address this problem with a novel mathematical model.

In this research, a bi-objective mixed-integer linear programming model is developed to tackle pre-positioning and distributing relief items, and it is formulated as an integrated location-allocation-routing problem with uncertain parameters. The humanitarian supply chain consists of relief facilities (RFs) and demand points (DPs). Perishable and imperishable relief commodities (RCs), different types of vehicles, different transportation modes, a time window for delivering perishable commodities and the occurrence of unmet demand are considered. A scenario-based game theory is applied for purchasing RCs from different suppliers and an integrated best-worst method-technique for order of preference by similarity to ideal solution technique is implemented to determine the importance of DPs. The proposed model is used to solve several random test problems for verification, and to validate the model, Iran’s flood in 2019 is investigated as a case study for which useful managerial insights are provided.

Managers can effectively adjust their preferences towards response time and total cost of the network and use sensitivity analysis results in their decisions.

The model locates RFs, allocates DPs to RFs in the pre-disaster stage, and determines the routing of RCs from RFs to DPs in the post-disaster stage with respect to minimizing total costs and response time of the humanitarian logistics network.

The current advancements in technologies and the internet industry provide users with many innovative digital devices for entertainment, communication and trade. However, simultaneous development and the rising sophistication of cybercrimes bring new challenges. Micro businesses use technology like how people use it at home, but face higher cyber risks during riskier transactions, with human error playing a significant role. Moreover, information security researchers have often studied individuals’ adherence to compliance behaviour in response to cyber threats. The study aims to examine the protection motivation theory (PMT)-based model to understand individuals’ tendency to adopt secure behaviours.

The study focuses on Australian micro businesses since they are more susceptible to cyberattacks due to the least security measures in place. Out of 877 questionnaires distributed online to Australian micro business owners through survey panel provider “Dynata,” 502 (N = 502) complete responses were included. Structural equational modelling was used to analyse the relationships among the variables.

The results indicate that all constructs of the protection motivation, except threat susceptibility, successfully predict the user protective behaviours. Also, increased cybersecurity costs negatively impact users’ safe cyber practices.

The study has critical implications for understanding micro business owners’ cyber security behaviours. The study contributes to the current knowledge of cyber security in micro businesses through the lens of PMT.