Artificial Intelligence (AI) is rapidly transforming the landscape of academic law libraries worldwide, offering new opportunities for enhancing legal research, information management, and user engagement. This article examines emerging trends in AI applications within academic law libraries, focusing on global developments alongside the unique challenges and opportunities faced in the Caribbean context. Key areas of exploration include AI-powered legal research tools, natural language processing (NLP) applications, and the ethical considerations surrounding AI integration. Drawing from insights presented at the CARALL Conference in July 2024, this article provides a comparative analysis of global best practices and proposes strategic recommendations for Caribbean academic law libraries to harness the potential of AI while addressing regional gaps in technological infrastructure and AI literacy.

Objectives/Goals: We developed an educational online module to equip researchers with knowledge, skills, and resources for conducting community-engaged research, aiming to foster meaningful collaboration between academia and communities. Methods/Study Population: A working group was formed, including three research faculty, four staff members, and four community partners who have partnered with researchers on community engaged projects. The working group first identified three objectives for the module and outlined what should be covered for each objective. The working group identified existing resources, texts, and videos that would address the objectives and worked in small groups to create additional content for the module. A smaller subgroup then took this content, organized it, and worked with the Office of Online Education to put the content into an interactive online format. Results/Anticipated Results: The three objectives identified for the online module are 1) Describe community engaged research, the purpose it serves, and why researchers do it; 2) Identify how to seek and collaboratively engage with a community partner; and 3) Identify and connect with resources for conducting community engaged research in Indiana. Each objective contains text, interactive figures and images, links to external resources or further reading, and videos of researchers and community partners talking about their own experiences and lessons learned. Each objective also includes activities and prompts for the learner to complete to apply the module content to the work they want to do. Discussion/Significance of Impact: Community engagement ensures research addresses real-world needs, builds trust, and includes diverse perspectives. Many researchers lack best practices to do this ethically. This module teaches skills needed to foster trust through transparency, respect, and by incorporating community voices.

We conduct the first modern econometric analysis of the historical deaf population in the United States by incorporating deafness into a model of human capital. We find that the deaf population invested less in observable educational and physical human capital. Lower literacy, employment, and occupational scores also suggest that unobserved human capital investments were not substantial enough to improve productivity to the level of the hearing population. States that subsidized schools for the deaf provided deaf people with improved social capital and access to intangible goods that they pursued at the cost of higher economic achievement. Finally, we argue that substantial lifecycle differences between the hearing and deaf populations have implications for unbiased school attendance and employment rate estimation.

This paper reports a series of experiments designed to evaluate how the advertised participation payment impacts participation rates in laboratory experiments. Our initial goal was to generate variation in the participation rate as a means to control for selection bias when evaluating treatment effects in common laboratory experiments. Initially, we varied the advertised participation payment to 1734 people from $\mathrm{\$}5$ to $\mathrm{\$}15$ using standard email recruitment procedures, but found no statistical evidence this impacted the participation rate. A second study increased the advertised payment up to $\mathrm{\$}100$. Here, we find marginally significant statistical evidence that the advertised participation payment affects the participation rate when payments are large. To combat skepticism of our results, we also conducted a third study in which verbal offers were made. Here, we found no statistically significant increase in participation rates when the participation payment increased from $\mathrm{\$}5$ to $\mathrm{\$}10$. Finally, we conducted an experiment similar to the first one at a separate university. We found no statistically significant increase in participation rates when the participation payment increased from $\mathrm{\$}7$ to $\mathrm{\$}15$. The combined results from our four experiments suggest moderate variation in the advertised participation payment from standard levels has little impact on participation rates in typical laboratory experiments. Rather, generating useful variation in participation rates likely requires much larger participation payments and/or larger potential subject pools than are common in laboratory experiments.

Longitudinal studies can provide insights into how family members negotiate the caring role and carer identity over time. The analyses of the longitudinal, qualitative interviews on ‘living well’ with dementia from the IDEAL cohort study aimed to identify the shifting, embedded narratives of family members of people with dementia as they negotiated the caring role and carer identity over time. Twenty semi-structured, qualitative interviews were conducted with family members of people with dementia and 14 were repeated one year later; these interviews were analysed using cross-sectional and longitudinal thematic and structural narrative analyses. Longitudinal, interrelated themes, including the care needs and decline of the person with dementia, relationship change and variable service support, framed the narrative types of family members. Six shifting narratives, apparent as dominant and secondary narrative types, characterized negotiating the caring role over time: absent/normalizing, active role adoption / carer identity, resistance, acceptance and resignation, hypervigilance/submergence and role entrapment, and foreshadowed future. The presence or absence of a carer identity was also evident from interviewees’ accounts, although, even where family members were overburdened by the caring role, they did not necessarily express a carer identity. Rather than considering transition into a carer identity, hearing different narratives within the caring role is important to understand how family members experience caring, whether they see themselves as ‘carers’, and when and how they need support. Timely and continued post-diagnostic support, where different caring narratives are recognized, is needed, as well as international initiatives for carer identification.

Klebsiella pneumoniae are opportunistic pathogens which can cause mastitis in dairy cattle. K. pneumoniae mastitis often has a poor cure rate and can lead to the development of chronic infection, which has an impact on both health and production. However, there are few studies which aim to fully characterize K. pneumoniae by whole-genome sequencing from bovine mastitis cases. Here, K. pneumoniae isolates associated with mastitis in dairy cattle were identified using matrix-assisted laser desorption/ionisation time-of-flight mass spectrometry (MALDI-TOF MS) and whole-genome sequencing. Furthermore, whole-genome sequence data were used for phylogenetic analyses and both virulence and antimicrobial resistance (AMR) prediction, in parallel with phenotypic AMR testing. Forty-two isolates identified as K. pneumoniae were subject to whole-genome sequencing, with 31 multi-locus sequence types being observed, suggesting the source of these isolates was likely environmental. Isolates were examined for key virulence determinants encoding acquired siderophores, colibactin, and hypermucoidy. The majority of these were absent, except for ybST (encoding yersiniabactin) which was present in six isolates. Across the dataset, there were notable levels of phenotypic AMR against streptomycin (26.2%) and tetracycline (19%), and intermediate susceptibility to cephalexin (26.2%) and neomycin (21.4%). Of importance was the detection of two ESBL-producing isolates, which demonstrated multi-drug resistance to amoxicillin-clavulanic acid, streptomycin, tetracycline, cefotaxime, cephalexin, and cefquinome.

For several years stigma researchers in India have relied on Western instruments or semi-structured stigma scales in their studies. However, these scales have not been rigorously translated and adapted to the local cultural framework. In the current study, we describe the cultural adaptation of six stigma scales with the purpose of using it in the native language (Kannada) based on translation steps of forward translation, expert review and synthesis, cultural equivalence, back translation and cognitive interview processes.

Several items were modified in the target language at each stage of the cultural adaptation process as mentioned in the above steps across all scales. Cultural explanations for the same have been provided. Concepts such as “community forest” and “baby sitting" was replaced with equivalent native synonyms. We introduced native cultural and family values such as “joint family system” and modified the item of housing concept in one of the tools. The concept of “privacy” in the Indian rural context was observed to be familial than individual-based and modification of corresponding items according to the native context of “privacy”. Finally, items from each scale were modified but retained without affecting the meaning and the core construct.

This article addresses a critical gap in international research concerning digital literacies and empowerment among adults who are English as an additional language (EAL) learners. In the Australian context, where digital communication and services are embedded in all aspects of life and work, proficiency in digital literacies, including advanced technologies like generative artificial intelligence (AI), is vital for working and living in Australia. Despite the increasing prevalence and significance of generative AI platforms such as ChatGPT, there is a notable absence of dedicated programs to assist EAL learners in understanding and utilising generative AI, potentially impacting their employability and everyday life. This article presents findings from a larger study conducted within training providers, spanning adult educational institutions nationwide. Through analysis of data gathered from surveys and focus groups, the article investigates the knowledge and attitudes of students, educators, and leaders regarding integrating generative AI into the learning program for adult EAL learners. The results reveal a hesitance among educators, particularly concerning beginning language learners, in incorporating generative AI into educational programs. Conversely, many adult learners demonstrate enthusiasm for learning about its potential benefits despite having limited understanding. These disparities underscore the pressing need for comprehensive professional development for educators and program leaders. The findings also highlight the need to develop the AI literacy of learners to foster their understanding and digital empowerment. The article concludes by advocating for a systemic approach to include generative AI as an important part of learning programs with students often from adult migrant and refugee backgrounds.

An extension is described to a product testing model to account for misinformation among subjects. A misinformed subject is one who associates the taste of product A with product B and vice-versa; thus, the subject would tend to perform incorrectly on pick 1 of 2 tests. A likelihood ratio test for the presence of misinformation is described. The model is applied to a data set, and misinformation is found to exist. Biases due to model misspecificationand other implications for product testing are discussed.