Search (25816 results, page 1290 of 1291)

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Abstract

For a special library serving its parent organization, the design and use of classification schemes primarily need to support work activities. However, when the Prince Vivadhanajaya Library at the Bank of Thailand decided to open its doors to the public in 2018, the redesign of classification that serves both internal staff work and the public interest became a challenging task. We designed a classification scheme by integrating work centered classification design approach, classification as communication framework and the service design approach. The design process included developing empathy, ideation and implementation and evaluation. As a result, the new classification scheme, including seven main classes and thirty-seven level-one subclasses and twenty-two level-two subclasses, was primarily based on the organization's strategic plans, mapping with JEL Classification Codes, Library of Congress Classification (LCC) and Library of Congress Subject Headings (LCSH). The classification scheme also includes geographical code, author cutter number, publication year, volume number and copy number. Follow up interviews with twenty-three participants were conducted two years later to evaluate user experience as well as the staff's opinion of the new classification scheme. The feedback addressed favorable outcomes and challenges to be used for the next iteration of the library service design process.

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Abstract

Data reuse has recently become significant in academia and is providing new impetus for academic research. This prompts two questions: What precisely is the data reuse process? What is the connection between each participating element? To address these issues, 42 studies were reviewed to identify the stages and primary data reuse elements. A meta-synthesis was used to locate and analyze the studies, and inductive coding was used to organize the analytical process. We identified three stages of data reuse-initiation, exploration and collection, and repurposing-and explored how they interact and form iterative characteristics. The results illuminated the data reuse at each stage, including issues of data trust, data sources, scaffolds, and barriers. The results indicated that multisource data and human scaffolds promote reuse behavior effectively. Further, two data and information search patterns were extracted: reticular centripetal patterns and decentralized centripetal patterns. Three paths with elements cooperating through flexible functions and motivated by different action items were identified: data centers, human scaffolds, and publications. This study supports improvements for data infrastructure construction, data reuse, and data reuse research by providing a new perspective on the effect of information behavior and clarifying the stages and contextual relationships between various elements.

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Abstract

Information behavior and practices (IBP) research has been repeatedly criticized for having little impact on information systems development (ISD). Claiming that there is a complete disconnect would be an exaggeration but it is apparent that it is not always easy to translate findings of IBP research to workable design recommendations. Based on a reading of earlier literature and a closer investigation of three illustrative example contexts, this article underlines that the value of IBP research for ISD lies in its capability to inform ISD of the variety of ways people deal with information beyond individual systems, their own wants and designers' assumptions. Moreover, it highlights that the implications of information systems go beyond their primary users. Instead of overemphasizing the contextuality of findings, a part of IBP research would benefit from an increased focus on explicating its epistemological extents and limits and identifying, which findings are transferable, what distinguishes specific contexts, what are their defining constraints and priorities, and what aspects of their uniqueness are assumptions and simple clichés.

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Abstract

This paper presents a contribution to the study of bibliographic corpora through science mapping. From a graph representation of documents and their textual dimension, stochastic block models can provide a simultaneous clustering of documents and words that we call a domain-topic model. Previous work investigated the resulting topics, or word clusters, while ours focuses on the study of the document clusters we call domains. To enable the description and interactive navigation of domains, we introduce measures and interfaces that consider the structure of the model to relate both types of clusters. We then present a procedure that extends the block model to cluster metadata attributes of documents, which we call a domain-chained model, noting that our measures and interfaces transpose to metadata clusters. We provide an example application to a corpus relevant to current science, technology and society (STS) research and an interesting case for our approach: the abstracts presented between 1995 and 2017 at the American Society of Clinical Oncology Annual Meeting, the major oncology research conference. Through a sequence of domain-topic and domain-chained models, we identify and describe a group of domains that have notably grown through the last decades and which we relate to the establishment of "oncopolicy" as a major concern in oncology.

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Abstract

Each section header of an article has its distinct communicative function. Citations from distinct sections may be different regarding citing motivation. In this paper, we grouped section headers with similar functions as a structural function and defined the distribution of citations from structural functions for a paper as its citation structure. We aim to explore the relationship between citation structure and the future impact of a publication and disclose the relative importance among citations from different structural functions. Specifically, we proposed two citation counting methods and a citation life cycle identification method, by which the regression data were built. Subsequently, we employed a ridge regression model to predict the future impact of the paper and analyzed the relative weights of regressors. Based on documents collected from the Association for Computational Linguistics Anthology website, our empirical experiments disclosed that functional structure features improve the prediction accuracy of citation count prediction and that there exist differences among citations from different structural functions. Specifically, at the early stage of citation lifetime, citations from Introduction and Method are particularly important for perceiving future impact of papers, and citations from Result and Conclusion are also vital. However, early accumulation of citations from the Background seems less important.

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Abstract

Scientific novelty drives the efforts to invent new vaccines and solutions during the pandemic. First-time collaboration and international collaboration are two pivotal channels to expand teams' search activities for a broader scope of resources required to address the global challenge, which might facilitate the generation of novel ideas. Our analysis of 98,981 coronavirus papers suggests that scientific novelty measured by the BioBERT model that is pretrained on 29 million PubMed articles, and first-time collaboration increased after the outbreak of COVID-19, and international collaboration witnessed a sudden decrease. During COVID-19, papers with more first-time collaboration were found to be more novel and international collaboration did not hamper novelty as it had done in the normal periods. The findings suggest the necessity of reaching out for distant resources and the importance of maintaining a collaborative scientific community beyond nationalism during a pandemic.

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Abstract

LEarning TO Rank (LETOR) algorithms are usually trained on annotated corpora where a single relevance label is assigned to each available document-topic pair. Within the Cranfield framework, relevance labels result from merging either multiple expertly curated or crowdsourced human assessments. In this paper, we explore how to train LETOR models with relevance judgments distributions (either real or synthetically generated) assigned to document-topic pairs instead of single-valued relevance labels. We propose five new probabilistic loss functions to deal with the higher expressive power provided by relevance judgments distributions and show how they can be applied both to neural and gradient boosting machine (GBM) architectures. Moreover, we show how training a LETOR model on a sampled version of the relevance judgments from certain probability distributions can improve its performance when relying either on traditional or probabilistic loss functions. Finally, we validate our hypothesis on real-world crowdsourced relevance judgments distributions. Overall, we observe that relying on relevance judgments distributions to train different LETOR models can boost their performance and even outperform strong baselines such as LambdaMART on several test collections.

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Abstract

A pidgin metadata framework based on the concept of pidgin metadata is proposed to complement the limitations of existing approaches to metadata interoperability and to achieve more reliable metadata interoperability. The framework consists of three layers, with a hierarchical structure, and reflects the semantic and structural characteristics of various metadata. Layer 1 performs both an external function, serving as an anchor for semantic association between metadata elements, and an internal function, providing semantic categories that can encompass detailed elements. Layer 2 is an arbitrary layer composed of substantial elements from existing metadata and performs a function in which different metadata elements describing the same or similar aspects of information resources are associated with the semantic categories of Layer 1. Layer 3 implements the semantic relationships between Layer 1 and Layer 2 through the Resource Description Framework syntax. With this structure, the pidgin metadata framework can establish the criteria for semantic connection between different elements and fully reflect the complexity and heterogeneity among various metadata. Additionally, it is expected to provide a bibliographic environment that can achieve more reliable metadata interoperability than existing approaches by securing the communication between metadata.

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Abstract

An exploratory study investigated the factors affecting digital literacy training offered by local community organizations, such as public libraries. Theory based on the educational assessment and information literacy instruction literatures, community informatics, and situated learning theory served as a lens of investigation. Case studies of two public libraries and five other local community organizations were carried out. Data collection comprised: one-on-one interviews with administrators, instructors, and community members who received training; analysis of training documents; observations of training sessions; and a survey administered to clients who participated in these training sessions. Data analysis yielded the generation of a holistic conceptual framework. The framework identifies salient factors of the learning environment and program components that affect learning outcomes arising from digital literacy training led by local community organizations. Theoretical propositions are made. Member checks confirmed the validity of the study's findings. Results are compared to prior theory. Recommendations for practice highlight the need to organize and train staff, acquire sustainable funding, reach marginalized populations, offer convenient training times to end-users, better market the training, share and adopt best practices, and better collect and analyze program performance measurement data. Implications for future research also are identified.

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Abstract

Records are persistent representations of activities created by partakers, observers, or their authorized proxies. People are generally willing to trust vital records such as birth, death, and marriage certificates. However, conspiracy theories and other misinformation may negatively impact perceptions of such documents, particularly when they are associated with a significant person or event. This paper explores the relationship between archival records and trustworthiness by reporting results of a survey that asked genealogists about their perceptions of 44th U.S. President Barack Obama's birth certificate, which was then at the center of the "birtherism" conspiracy. We found that although most participants perceived the birth certificate as trustworthy, others engaged in a biased review, considering it not trustworthy because of the news and politics surrounding it. These findings suggest that a conspiracy theory can act as a moderating variable that undermines the efficacy of normal or recommended practices and procedures for evaluating online information such as birth certificates. We provide recommendations and propose strategies for archivists to disseminate correct information to counteract the spread of misinformation about the authenticity of vital records, and we discuss future directions for research.

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Abstract

Contributing to the literature on knowledge infrastructure maintenance, this article describes a historical longitudinal analysis of revenue streams employed by four social science data organizations: the Roper Center for Public Opinion, the Inter-university Consortium for Political and Social Research (ICPSR), the UK Data Archive (UKDA), and the LIS Cross-National Data Center in Luxembourg (LIS). Drawing on archival documentation and interviews, we describe founders' assumptions about revenue, changes to revenue streams over the long term, practices for developing and maintaining revenue streams, the importance of financial support from host organizations, and how the context of each data organization shaped revenue possibilities. We extend conversations about knowledge infrastructure revenue streams by showing the types of change that have occurred over time and how it occurs. We provide examples of the types of flexibility needed for data organizations to remain sustainable over 40-60 years of revenue changes. We distinguish between Type A flexibilities, or development of new products and services, and Type B flexibilities, or continuous smaller adjustments to existing revenue streams. We argue that Type B flexibilities are as important as Type A, although they are easily overlooked. Our results are relevant to knowledge infrastructure managers and stakeholders facing similar revenue challenges.

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Abstract

This article presents approaches used to solve the problem of selecting AI technologies and tools to obtain the creativity fostering functionalities of an innovative knowledge platform. The aforementioned selection problem has been lagging behind other software-specific aspects of online knowledge platform and learning platform development so far. We linked technological recommendations from group decision support exercises to the platform design aims and constraints using an expert Delphi survey and multicriteria analysis methods. The links between expected advantages of using selected AI building tools, AI-related system functionalities, and their ongoing relevance until 2030 were assessed and used to optimize the learning scenarios and in planning the future development of the platform. The selected technologies allowed the platform management to implement the desired functionalities, thus harnessing the potential of open innovation platforms more effectively and delivering a model for the development of a relevant class of advanced open-access knowledge provision systems. Additionally, our approach is an essential part of digital sustainability and AI-alignment strategy for the aforementioned class of systems. The knowledge platform, which serves as a case study for our methodology has been developed within an EU Horizon 2020 research project.

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Abstract

Although there have been several studies of people's information behaviors during the COVID-19 epidemic, the information practices of a specific group of people-those with depression-have been neglected. This study reports on qualitative interviews with 24 participants to explore the information practices of people living with depression during the pandemic lockdown. We use the theory of information worlds and the concept of transition to understand the phases of chaos, expansion, and contraction that the information worlds of this group present during the lockdown, and examine the interrelationship between the information worlds and the individual's transition experiences during specific periods. Our results show that, first, emotion, body, and embodiment play key roles in the individual's information worlds, while individuals' information practices are regulated by the economic environment, group norms, and other social circumstances. Second, government-level information, the volunteer community, social media, and nonhuman objects further impact participants' information practices. We suggest that health management strategies need to have different priorities at different stages of the transition, and attention should be paid to the provision of emotional information support systems during the pandemic lockdown.

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Abstract

There are several ways to employ machine learning for automating subject indexing. One popular strategy is to utilize a supervised learning algorithm to train a model on a set of documents that have been manually indexed by subject matter using a standard vocabulary. The resulting model can then predict the subject of new and previously unseen documents by identifying patterns learned from the training data. To do this, the first step is to gather a large dataset of documents and manually assign each document a set of subject keywords/descriptors from a controlled vocabulary (e.g., from Agrovoc). Next, the dataset (obtained from Agris) can be divided into - i) a training dataset, and ii) a test dataset. The training dataset is used to train the model, while the test dataset is used to evaluate the model's performance. Machine learning can be a powerful tool for automating the process of subject indexing. This research is an attempt to apply Annif (http://annif. org/), an open-source AI/ML framework, to autogenerate subject keywords/descriptors for documentary resources in the domain of agriculture. The training dataset is obtained from Agris, which applies the Agrovoc thesaurus as a vocabulary tool (https://www.fao.org/agris/download).

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Abstract

Knowledge of ovarian cancer patients' information practices around cancer genetic testing (GT) is needed to inform interventions that promote patient access to GT-related information. We interviewed 21 ovarian cancer patients and survivors who had GT as part of the treatment process and analyzed the transcripts using the qualitative content analysis method. We found that patients' information practices, manifested in their information-seeking mode, information sources utilized, information assessment, and information use, showed three distinct styles: passive, semi-active, and active. Patients with the passive style primarily received information from clinical sources, encountered information, or delegated information-seeking to family members; they were not inclined to assess information themselves and seldom used it to learn or influence others. Women with semi-active and active styles adopted more active information-seeking modes to approach information, utilized information sources beyond clinical settings, attempted to assess the information found, and actively used it to learn, educate others, or advocate GT to family and friends. Guided by the social ecological model, we found multiple levels of influences, including personal, interpersonal, organizational, community, and societal, acting as motivators or barriers to patients' information practice. Based on these findings, we discussed strategies to promote patient access to GT-related information.

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Series

Reviews of concepts in knowledge organization

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Abstract

The Korean Decimal Classification (KDC) and Nippon Decimal Classification (NDC) are national classification systems of Korea and Japan. They have been used widely in many libraries of each country and maintained successfully by each national library associations of Korean Library Association (KLA) and Japan Library Association (JLA). This study compares the general characteristics of these two national classification systems using their latest editions of KDC 6 and NDC 10. After reviewing the former research, their origins, general history and development, and usages were briefly compared. Various aspects including classification by discipline, not by subjects, decimal expansion of the classes using pure notations of Arabic, hierarchical structure, and mnemonics quality are checked for both systems. Results of the comparative analyses of major auxiliary tables, main classes and 100 divisions of schedules of two systems are suggested one by one with special regards to Dewey Decimal Classification (DDC). The analyses focus on the differences between both systems as well as the characteristics which reflect the local situations of both countries. It suggests some ideas for future developments and research based on the results of their strengths and weaknesses.

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Abstract

People search for information and experiences and seek meaning as a common reaction to new life challenges. There is little knowledge about the interactions through which experiential information is acquired, and how such interactions are meaningful to an information seeker. Through a qualitative content analysis of 992 posts in an online forum, this study investigated lesbian, gay, bisexual, transgender, intersex, queer/questioning, asexual (LGBTQIA+) individuals' online information interactions and meaning-making with peers during their life transitions of identity construction. Our analysis reveals LGBTQIA+ people's life challenges across three transition stages (being aware of, exploring, and living with a new identity). Three main types of online peer interactions were identified within: cognitive, affective, and situational peer interactions. We found that online peer interactions are not only a type of information source that LGBTQIA+ individuals use to acquire understanding about themselves but a unique space for transformation learning and meaning-making where they share self-examination and reflection, conduct assessments and assumptions, and obtain strength and skills to initiate and adapt life transitions. The findings have theoretical contributions to the development of information behavior models of transitions and practical implications on providing information services that support LGBTQIA+ individuals' meaning-making during the life transition.

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Abstract

Improving health literacy through health information dissemination is one of the most economical and effective mechanisms for improving population health. This process needs to fully accommodate the thematic suitability of health information supply and demand and reduce the impact of information overload and supply-demand mismatch on the enthusiasm of health information acquisition. We propose a health information topic modeling analysis framework that integrates deep learning methods and clustering techniques to model the supply-side and demand-side topics of health information and to quantify the thematic alignment of supply and demand. To validate the effectiveness of the framework, we have conducted an empirical analysis on a dataset with 90,418 pieces of textual data from two prominent social networking platforms. The results show that the supply of health information in general has not yet met the demand, the demand for health information has not yet been met to a considerable extent, especially for disease-related topics, and there is clear inconsistency between the supply and demand sides for the same health topics. Public health policy-making departments and content producers can adjust their information selection and dissemination strategies according to the distribution of identified health topics, thereby improving the effectiveness of public health information dissemination.