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

This study uses a theoretical lens of infrastructural dimensions to examine stakeholders' perceptions of the value of curation, focusing on the social science data repository, the Inter-university Consortium for Political and Social Research (ICPSR). Drawing on 67 interviews with both internal (ICPSR staff) and external (funders, data producers, and reusers) stakeholders, we analyze how value is ascribed to curation across technical, organizational, and social components of infrastructure. We identify five key ways interviewees conceptualized the value of curation infrastructures: supporting sustainability and durability, enabling research efficiency, fostering trust, building community, and advancing data equity. Our findings highlight the role of curation in knowledge generation by reframing curation as infrastructure rather than a set of discrete practices. We clarify how transparency operates in dual—and sometimes conflicting—ways: as both understandability and invisibility, shaping trust in and access to data repositories. Second, we demonstrate how data equity is increasingly perceived by stakeholders as a core infrastructural value, enacted through practices that lower barriers to access. Finally, we surface the persistent challenges in evaluating and funding curation infrastructures due to their long time horizons and often-invisible nature. This work advocates recognizing and funding curation infrastructures as essential for long-term scientific and societal progress.

Scientific text classification is essential for efficiently organizing and assimilating scientific knowledge. However, existing methods struggle to classify ultra-short scientific texts due to their limited content and complex hierarchical labeling. To overcome these challenges, we introduce the BERT-HMCN framework, which combines Bidirectional Encoder Representations from Transformers (BERT) with a Hierarchical Multi-label Classification Network (HMCN). This framework introduces a novel level-fixed fine-tuning strategy that strengthens the connection between text semantics and hierarchical labels, enhancing the representation of ultra-short texts. We evaluated BERT-HMCN's performance on a dataset of 75,065 program titles from the National Natural Science Foundation of China. Our results show that BERT-HMCN outperforms existing models in both overall performance and hierarchical accuracy. We also conducted a comparative analysis with autoregressive large language models (LLMs), illustrating the strengths of each in different contexts. Further analysis confirms the effectiveness and robustness of the BERT-HMCN framework. We discuss its theoretical contributions and practical applications, underscoring the broader implications of these results in scientific text classification and other related fields.

Researchers increasingly share data, both on their own initiative and as a result of requirements by funding agencies and publishers. For data to be accessible and reusable, it must be understandable. While typical metadata covers rudimentary information about data, data re-users often need more contextual information, including paradata informative of data-related practices and processes. To better understand the practices and types of data descriptions researchers produce, this paper analyzes 33 interviews with researchers and professionals working with archeological data in different capacities. We identified five data description practices: (1) prescribing, (2) keeping track, (3) describing (of what was done (processes); of structures, techniques, methods; of principles, rationales, decisions; of limitations of data), (4) flagging, and (5) publishing, formatting, and making available. A part evinces integrated paradata creation where paradata generation is tightly incorporated in the enactment of specific research methods, and a part standalone paradata creation prompted by aspirations to produce specific types of outputs. The findings suggest that underpinning instrumentalities, and the extent to which paradata creation is integral to research practice is central when developing means to support paradata generation, identifying where to find and how to manage it.

Citations indexes play a crucial role for understanding how science is produced, disseminated, and used. However, these databases often face a critical trade-off: those offering extensive and high-quality coverage are typically proprietary, whereas publicly accessible datasets frequently exhibit fragmented coverage and inconsistent data quality. OpenAlex was developed to address this challenge, providing a freely available database with broad open coverage, with a particular emphasis on non-English speaking countries. Yet, few studies have assessed the quality of the OpenAlex dataset. This paper assesses the coverage by OpenAlex of China's papers, which shows an abnormal trend, and compares it with other countries that do not have English as their main language. Our analysis reveals that while OpenAlex increases the coverage of China's publications, primarily those disseminated by a national database, this coverage is incomplete and discontinuous when compared to other countries' records in the database. We observe similar issues in other non-English-speaking countries, with coverage varying across regions. These findings indicate that although OpenAlex expands coverage of research outputs, continuity issues persist and disproportionately affect certain countries. We emphasize the need for researchers to use OpenAlex data cautiously, being mindful of its potential limitations in cross-national analyses.

With the growing ubiquity of virtual collaboration, it is important to understand what contributes to effective online teamwork. In large, online social systems, public, task-related discussions are vital for effectiveness, but direct, interpersonal communication may also play a role. We hypothesize that the positive effects of direct communication on online teamwork may be due to its role in building social capital. To verify this proposition, we analyzed network properties of interpersonal communication among Wikipedia editors, employing novel measures from network science - Effective Information - to measure social capital. We discovered that groups producing high-quality articles have communication networks that allow for local sharing of knowledge as well as for integration of information among the whole group: a structure promoting high social capital. Our results underscore the importance of direct communication for groups collaborating online and suggest that platforms for such communities should allow for ample one-on-one interactions.

Academic libraries play an increasingly crucial role in providing services, information, education, and infrastructure support related to research data management (RDM). This systematic review aims to provide a comprehensive and critical analysis of the state of RDM services offered by academic libraries worldwide. Utilizing the systematic review methodology, the paper examines 89 empirical studies to answer four research questions: (1) the types of RDM services implemented by academic libraries; (2) what are the infrastructure, workflow, and resources used to support these services; (3) what are the reasons for implementing these RDM services; and (4) the effectiveness of these RDM services in supporting the research data life cycle, if any. This review highlights the critical reasons academic libraries provide RDM services and how they implemented these services through partnerships, infrastructure, and systems, and adapting to new workflows within the library. These findings also examine the balance between institutional contexts, researchers' needs, and library resources required to provide these RDM services. By investigating these questions, the results will provide recommendations and guidance for academic libraries interested in implementing RDM services in their own library and institutional contexts.

Citation analysis, an essential method in bibliometrics and scientometrics, has been widely applied to assess scholarly publications. Traditionally, studies attribute citation contributions directly to cited papers, overlooking indirect citations. Citation cascade research improves on this by assuming papers inherit contributions from their citation generations but does not consider the source of the citation content (CC). We argue that citation contributions should be attributed to the source of the CC, which includes not only the cited paper but also its references. This study introduces a semantic similarity-driven approach (CCA_SS_RS) to allocate citation contributions. CCA_SS_RS evaluates semantic similarity between CC in the citing paper (CC_FP) and the reference span in the cited paper (RS_FP), as well as between CC_FP and CCs associated with the cited paper's references (CC_{FP_R}). If similarity between CC_FP and CC_{FP_Ri} exceeds or equals that between CC_FP and RS_FP, the i-th reference is credited; otherwise, the cited paper receives full credit. Tested on the CL-SciSumm 2017 dataset, CCA_SS_RS outperformed three established methods in identifying implicit cited sources, enabling references to receive varying contributions based on semantic similarity. This study highlights the significant impact of citation contribution attribution on paper evaluation and ranking.

Blockchain technology has drawn growing attention in the literature and in practice. Blockchain technology generates considerable amounts of data and has thus been a topic of interest for machine learning (ML). The objective of this paper is to provide a comprehensive review of the state of the art on ML applied to on-chain data. This work aims to systematically identify, analyze, and classify the literature on ML applied to blockchain data. This will allow us to discover the fields where more effort should be placed in future research. A systematic mapping study has been conducted to identify the relevant literature. Ultimately, 211 articles were selected and classified according to various dimensions, specifically the domain use case, the blockchain, the data, and the ML models. The majority of the papers (43.35%) fall within the Anomaly use case. Ethereum (46.31%) was the blockchain that drew the most attention. A dataset consisting of more than 1,000,000 data points was used by (29.06%) of the papers. Classification (43.84%) was the ML task most applied to on-chain data. The results confirm that ML applied to on-chain data is a relevant and a growing topic of interest both in the literature and in practice. Researchers have studied interesting use cases such as address classification, anomaly detection, cryptocurrency price prediction, performance evaluation, and smart contract vulnerability detection. Nevertheless, some open challenges and gaps remain, which can lead to future research directions. Specifically, we identify novel ML algorithms, the lack of a standardization framework, blockchain scalability issues, and cross-chain interactions as areas worth exploring in the future.

The term semantic primitives refers to a set of basic, atomic concepts from which all other (compound) concepts are constructed. It presupposes the principle of compositionality—the idea that complex items or expressions can be formed by combining simpler constituents. Both notions are of particular relevance to knowledge organization (KO), where concepts are understood to be the primary objects of organization in knowledge organization systems (KOS). Semantic primitives, therefore, may be viewed as candidates for foundational units in such systems. Moreover, these concepts play important roles in fields such as automatic language processing, lexicography, word sense disambiguation, and artificial intelligence. In KO, they relate to methods such as semantic factoring and facet analysis, while in linguistics they parallel componential analysis. Nevertheless, semantic primitives and compositionality remain controversial, with strong arguments both for and against their very existence. The philosophical assumptions underlying these debates have significant implications for information science and knowledge organization.

People frequently experience difficulties when seeking information to complete tasks. To overcome these difficulties, people require help. Regarding struggles with information needs, past research focuses on unclear information requests, such as ambiguous, under-specified, and ill-defined queries, and repairing these by user-led strategies (e.g., clarification). In an exploratory qualitative study where information clerks were interviewed, we, however, found that well-formed and seemingly reasonable requests can conceal misconceptions inquirers have (e.g., about what information is required for their current task) and, therefore, interfere with information seeking and task completion, too. Besides being more difficult to identify than unclear requests, such hidden misconceptions also undermine current user-led repair strategies as they cause inquirers to believe they are making appropriate requests. Understanding misconceptions in information seeking and requests concealing these is, therefore, essential to building more effective information systems. Our study contributes to addressing this task: It is the first to provide empirical insights into how misconceptions can negatively influence information requests, information-seeking conversations, and task completion. Ultimately, our findings highlight that inquirers' perceived information needs can present an unreliable and even counterproductive basis for task support, implying that researchers and professionals should rethink the prevailing focus on user requests in designing information systems.