Journal of the Association for Information Science and Technology最新文献

In recent decades, the proliferation of data and advances in information technology have led organizations to value data more highly and aim to build a data culture that is suitable for promoting and sustaining data-related strategic outcomes. However, what a “good” data culture comprises is often expressed abstractly and there is no consensus about how such a culture should manifest in practice. This study explores the key dimensions and attributes of an ideal data culture, as perceived by expert practitioners in large, data-rich public sector organizations. Using a two-stage Delphi method, we engaged with 14 data management experts from Aotearoa New Zealand to understand their views on achieving “Data Nirvana” in practice, focusing on the attributes that explain an ideal data culture. Five categories of ideal data culture are identified: strategic agility, ethical use, human centricity, capability, and controls and discipline. These are linked through two unifying themes: trust and trustworthiness, and value integration. The resulting framework for data culture comprises seven elements. The study provides insights into the aspirational potential of data and the realities of organizational data practice, contributing to a deeper understanding of data culture.

New knowledge builds upon existing foundations, which means an interdependent relationship exists between knowledge, manifested in the historical records of the scientific system for hundreds of years. By leveraging natural language processing techniques, this study introduces the Scientific Concept Navigator, an embedding-based navigation model to infer the “knowledge pathway” from the research trajectories of millions of scholars. We validate that the learned representations effectively delineate disciplinary boundaries and capture the intricate relationships between diverse concepts. Utility of the navigation space is showcased through multiple applications. Firstly, we demonstrate the multi-step analogy inferences between concepts from various disciplines. Secondly, we formulate the cross-domain conceptual dimensions of knowledge, observing the distributional shifts of 19 disciplines along these conceptual dimensions, including “Theoretical” to “Applied,” and “Societal” to “Economic,” highlighting the evolution of functional attributes across diverse domains. Lastly, by analyzing the knowledge network structure, we find that knowledge connects with shorter global pathways, and interdisciplinary concepts play a critical role in enhancing accessibility. Our framework offers a novel approach to mining knowledge inheritance pathways from extensive scientific literature, which is of great significance for understanding scientific progression patterns, tailoring scientific learning trajectories, and accelerating scientific progress.

The rise of social media has brought new dynamics to the dissemination of scientific research, with Twitter (X) playing a significant role. This study focuses on the role of social bots—automated accounts designed to mimic human behavior and amplify content—in scientific communication. By analyzing over 3.7 million papers published between 2017 and 2021 and their 51 million Twitter mentions. Using a novel hybrid method that includes BotometerLite and specific activity parameters, with verification via a robustness check, it was found that 0.23% of accounts were bots. Despite their small numbers, these bots contributed to 4.72% of all mentions, indicating a significant presence, but with varied impact. Bots were particularly active in Mathematics, Physics, and Space Sciences, where they generated over 70% of tweets in some cases. Automated accounts disproportionately influence the visibility and perceived impact of research in these disciplines, which underscores the need for discipline-specific analysis when considering Twitter's role in scientific communication. This large-scale study highlights the potential for bots to skew altmetric indicators, misleading stakeholders about true engagement.

The importance of evidence-based policymaking is widely recognized, but how science influences policy remains insufficiently explored. This study aims to examine how policy documents cite research articles, thereby tracing the complex impact process of scientific research on policymaking. A conceptual model is proposed to classify four types of citation pathways by distinguishing between direct and indirect impacts and observing whether a reinforcement effect is present. To operationalize this model, we collected nearly 10 thousand policy documents related to artificial intelligence (AI) and over 1.6 million links between these policies and their referenced articles. A large-scale data analysis and a case study were conducted. Results exhibit distinct citation pathways among specific types of institutions, geopolitical areas, and policy areas. Indirect influences emerge as an important mechanism. Research articles from EU countries primarily serve the policymaking of inter-governmental organizations (IGOs) and the EU, while research articles from the USA significantly support both domestic and foreign policymaking. Notably, IGOs serve as key intermediaries, facilitating the indirect influence of research on policymaking. In addition, while the knowledge from the social sciences provides substantial support for policies in various areas, an increasing involvement of the natural sciences in the development of AI-related policies is found.

Research funding plays a crucial role in the production of knowledge, and its nature varies considerably from country to country. Numerous studies have analyzed research funding from a bibliometric perspective. However, the role of individual authors in attracting funding remains understudied, and it may be crucial for many actors. We propose a new approach that provides a more accurate picture and test it on post-Soviet countries with low scientific production. We analyze the funding sources of the most visible part of the natural sciences by focusing on the funding acknowledgments of their papers in Nature Index journals published in 2017–2021. Both the country of origin and types of sources are accounted for. Our approach reveals marked differences between traditionally used paper-level and proposed author-level funding links. The shares of funding sources measured in this way are very different, especially with regard to foreign sources and the role of specific countries. This is particularly important when studying international papers and the roles of the countries involved, even more so for the countries with lower research capacity. Utilizing a case-driven funding sources classification, we paint a rich picture of diverging post-Soviet funding landscapes, mostly driven by national grants and EU-wide programmes.

When faced with significant life events, people often seek information support to help them regain a sense of meaning. Previous research has rarely explored the information practices of people with mental illnesses, particularly those with obsessive-compulsive disorder (OCD) during life transitions. In this study, we conducted qualitative interviews with 23 people with OCD, using the critical incident technique, to explore their transitional information practices during mental health challenges and to understand how these practices support their meaning-making processes. An integrated theoretical perspective was proposed, drawing on the information behavior theory of transitions and activity theory, to understand the interplay between the activities of people with OCD and the three transitional stages of understanding, negotiating, and resolving. These activities are influenced by a complex interplay of embodied experiences, social connections, cultural norms, and practical or abstract artifacts, which in turn shape the transitional information practices of individuals with OCD. Consequently, we constructed a model of the transitional information practices among individuals with OCD. This study contributes to the literature on information practices and meaning-making during life transitions and provides practical insights into how individuals with OCD might receive information support and interventions from various communities.

People with disabilities face barriers when engaging with information retrieval (IR) systems due to designs that overlook their needs. This systematic literature review explores research for individuals with disabilities interacting with IR systems. Relevant theories concerning disabilities were examined, and the gap model was used as the theoretical framework that guided the review. This review covers relevant research published from 2000 to 2023, focusing on user groups with sensory, cognitive, and motor impairments. The main topics are help-seeking situations encountered by these user groups in various IR systems due to system design not meeting user needs, and search tactics applied by users with different types of disabilities corresponding to various help-seeking situations. Design recommendations for IR systems and platforms were also examined. Key limitations in existing research and the authors' reflections are highlighted, including a lack of theories on the interactions between people with disabilities and IR systems, imbalanced research on and misclassification between different types of impairments, unclear distinctions between accessibility and usability, unexplored IR issues in mobile environments, and inadequate existing IR system designs, along with the challenges posed by one-size-fits-all design. Further research opportunities are also proposed.

Academic libraries, institutions, and publishers are interested in predicting future publishing output to help evaluate publishing agreements. Current predictive models are overly simplistic and provide inaccurate predictions. This paper presents Bayesian and frequentist statistical models to predict future article counts and costs. These models use the past year's counts of corresponding authored peer-reviewed articles to predict the distribution of the number of articles in a future year. Article counts for each journal and year are modeled as a log-linear function of year with journal-specific coefficients. Journal-specific predictions are summed to predict the distribution of total paper count and combined with journal-specific costs to predict the distribution of total cost. We fit models to three data sets: 366 Wiley journals for 2016–2020, 376 Springer-Nature journals from 2017 to 2021, and 313 Wiley journals from 2017 to 2021. For each dataset, we compared predictions for the subsequent year to actual counts. The model predicts two datasets better than using either the annual mean count or a linear trend regression. For the third, no method predicts output well. A Bayesian model provides prediction uncertainties that account for all modeled sources of uncertainty. Better estimates of future publishing activity and costs provide critical, independent information for open publishing negotiations.

Data science has many articulation points with information science, both in academic research contexts and in professional situations. Several recent journal special issues show the need for reflexivity in identifying and further building out these articulation points. In this brief communication, I outline aspects of data science that were not extensively discussed in detail within these special issues and deserve more attention from the JASIST community. I discuss how the information science community has important roles in building stronger theoretical understanding of data and data science, developing a more detailed understanding of the data science publishing landscape, and in mapping different manifestations of data science across societal sectors. Information science-informed work in these areas will enable further understanding of data and data science as academic and societal phenomena.

Using intersectionality as a critical theoretical framework and analytical tool, this study investigated the HIV/AIDS information practices of Black sexual minority men (SMM). Twenty-two Black SMM were interviewed about their HIV/AIDS-related information practices. The resulting data were analyzed inductively using methods influenced by constructivist grounded theory. I propose information resilience as a strengths-based concept to describe protective and promotive information practices that focus on meeting individual or community-centric goals despite intersectional stigma and discrimination. Anticipated and experienced intersectional stigma and discrimination were the key motivators for protective information practices among Black SMM. Promotive factors, including peer support and self-efficacy, shaped promotive information practices to foster development and enhance well-being. The findings have implications for the incorporation of intersectionality theory into information practices research, contribute to theoretical development in the field of library and information science, and have implications for the design of information and technology-based HIV prevention and treatment interventions to address intersectional discrimination and its impact on Black sexual minority men.