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This article provides a scoping review of 25 years of research on the notion of cognitive authority (CA), examining its conceptualization and empirical examination. The review follows the PRISMA statement and its extension for scoping reviews. Peer‐reviewed journal articles on CA were identified through database searching with the specific search term “cognitive authorit*” in the title or abstract and covering work published in 2022 or earlier. In total, 235 unique references were identified, and their abstracts and then selected full texts were screened according to predetermined exclusion criteria. In total, 40 articles were included in the review, extracted, and analyzed with qualitative content analysis focusing on the conceptualization of CA, the methodological approach taken to examine it, and the different spheres of knowledge and levels of activity the research addressed. Based on this analysis, four parallel lines of research were identified including studies conceptualizing CA: (1) as an indicator of information source quality, (2) as discursively constructed, (3) as situated in social mechanisms and settings, and (4) as institutional legitimacy of science and professions. This body of research has extended Wilson's (1983; Second‐hand knowledge: An inquiry into cognitive authority. Greenwood Press) original work contributing to our understanding of CA at individual, communal, and societal levels. [ABSTRACT FROM AUTHOR]

Research on how people look for and interact with information has a long history in the information field. The current literature has been repeatedly reviewed in earlier volumes of Annual Review of Information Science and Technology. In this review, we offer an overview of the research published in this area in the years 2016–2022 with a focus on the trends that have emerged in this period. We use the term “information behavior” as an umbrella for the research area interested in how people become informed and engage with information in diverse manners acknowledging that different researchers and subfields prefer other terms and frameworks, including information practices, information experience, and health information seeking, to name a few. We reviewed 1270 articles in the field published in the years 2016–2022 and identified seven emerging trends: The CoVID‐19 Pandemic, Diversity and Inclusion, Embodiment, Misinformation and Trust, Social Q&A Websites, Collaboration, and Information Creation. The reviewed literature and trends are discussed in relation to their significance for information, earlier review of information behavior research, and the long‐debated issue of theory‐driven versus atheoretical research in the field. [ABSTRACT FROM AUTHOR]

Identifying factors that associate with more cited or higher quality research may be useful to improve science or to support research evaluation. This article reviews evidence for the existence of such factors in article text and metadata. It also reviews studies attempting to estimate article quality or predict long‐term citation counts using statistical regression or machine learning for journal articles or conference papers. Although the primary focus is on document‐level evidence, the related task of estimating the average quality scores of entire departments from bibliometric information is also considered. The review lists a huge range of factors that associate with higher quality or more cited research in some contexts (fields, years, journals) but the strength and direction of association often depends on the set of papers examined, with little systematic pattern and rarely any cause‐and‐effect evidence. The strongest patterns found include the near universal usefulness of journal citation rates, author numbers, reference properties, and international collaboration in predicting (or associating with) higher citation counts, and the greater usefulness of citation‐related information for predicting article quality in the medical, health and physical sciences than in engineering, social sciences, arts, and humanities. [ABSTRACT FROM AUTHOR]

Value co‐creation as a research topic has been a critical proposition of interest to a wide range of disciplines. In recent years, the field of cultural heritage has also developed a range of information practices to promote different forms of value co‐creation. While the body of literature has begun to accumulate, we still lack a comprehensive understanding of conceptual foundations and how value co‐creation is implemented in cultural heritage information practices. This paper conducted a hermeneutic literature review to understand and conceptualize the core aspects of value co‐creation in various cultural heritage information works. In particular, this paper provides a research mapping of the genres of co‐creation in cultural heritage information practices, the actors, and the roles played by the stakeholders. Further, this review provides an overview of the dominant value co‐creation models in the cultural heritage field. It distills the drivers of value co‐creation and the core value consequences that arise from a process perspective. Finally, this paper puts forward a thematic, theoretical, and methodological agenda for future work based on these findings. The review contributes to the literature by proposing an integrated conceptual framework that summarizes the core elements of value co‐creation in cultural heritage information practices. The framework also serves as a call for action, providing a general understanding of the conceptual foundation for future research. [ABSTRACT FROM AUTHOR]

While bibliographic classifications are traditionally based on disciplines, the logical alternative is phenomenon‐based classification. Although not prevalent, this approach has been explored in the 20th century by J.D. Brown, the Classification Research Group, and others. Its principles have been stated in the León Manifesto (2007) and are currently represented by such general schemes as the Basic Concepts Classification and the Integrative Levels Classification. A phenomenon‐based classification lists classes of phenomena, including things and processes irrespective of the discipline studying them (which can optionally be specified as an additional facet). Facets can work in a phenomenon‐based system much as in a disciplinary one. This kind of system will promote the identification of potential relationships between research in different disciplines, and will especially benefit interdisciplinary work. The paper reviews the theory, history, structure, advantages, applications, and evaluation of phenomenon‐based classification systems. [ABSTRACT FROM AUTHOR]

The gig economy and gig work have grown quickly in recent years and have drawn much attention from researchers in different fields. Because the platform mediated gig economy is a relatively new phenomenon, studies have produced a range of interesting findings; of interest here are the socio‐technical issues that this work has surfaced. This systematic literature review (SLR) provides a snapshot of a range of socio‐technical issues raised in the last 12 years of literature focused on the platform mediated gig economy. Based on a sample of 515 papers gathered from nine databases in multiple disciplines, 132 were coded that specifically studied the gig economy, gig work, and gig workers. Three main socio‐technical themes were identified: (1) the digital workplace, which includes information infrastructure and digital labor that are related to the nature of gig work and the user agency; (2) algorithmic management, which includes platform governance, performance management, information asymmetry, power asymmetry, and system manipulation, relying on a diverse set of technological tools including algorithms and big data analytics; (3) ethical design, as a relevant value set that gig workers expect from the platform, which includes trust, fairness, equality, privacy, and transparency. A social informatics perspective is used to rethink the relationship between gig workers and platforms, extract the socio‐technical issues noted in prior research, and discuss the underexplored aspects of the platform mediated gig economy. The results draw attention to understudied yet critically important socio‐technical issues in the gig economy that suggest short‐ and long‐term opportunities for future research directions. [ABSTRACT FROM AUTHOR]

Data culture/s as a research topic has begun to attract attention from a wide range of disciplines, albeit with inconsistent application of definitions, dimensions, and applications. This work builds on a call to investigate data culture/s within the information studies domain as a topic related to, but distinct from, information culture. The purpose of this study is to explore what is known about data culture/s in greater depth. We apply a retroductive approach to select and consider likely dimensions, inputs, and aspects of data culture/s in order to further map this construct to the literature, and thereby highlight gaps and opportunities to add to this body of knowledge. The initial candidate dimensions explored below include data‐related skills and attitudes, data sharing, data use/reuse, data ethics and governance, and a specific focus on Indigenous perspectives to provide insights on why and how a group may contest the emergent dominant discourse of data culture/s. Our conclusion highlights areas needing further research to fully define and examine the dimensions, inputs, and aspects of data culture/s, and calls for greater understanding and engagement with data culture/s from the information studies community. [ABSTRACT FROM AUTHOR]

Death is an inevitable part of life and highly relevant to information management: its approach often requires preparation, and its occurrence often demands a response. Many works in information science have acknowledged so much, and yet death is rarely a focused topic, appearing instead sporadically and disconnected across research. As a result there is no introduction to, overview of, or synthesis across studies on death and information. We therefore conducted an extensive literature search and reviewed nearly 300 scholarly publications at the intersection of death and information (and data) management. Covering seven topics in total, we review two groups of work directly engaging information management in relation to death (digital possessions, inheritance, and legacy; information behavior, needs, and practices around death), three engaging death and technology that require information and its management (death and the Internet, thanatosensitive design and technology‐augmented death practices, and the digital afterlife and digital immortality), and two reflecting the ethical and legal dimensions unique to death and information. We then integrate the collective findings to summarize the landscape of death‐related information research, outline remaining challenges for individuals, families, institutions, and society, and identify promising directions for future information science research. [ABSTRACT FROM AUTHOR]

Reviews have long been recognized as among the most important forms of scientific communication. The rapid growth of the primary literature has further increased the need for reviews to distill and interpret the literature. This review on Reviews and Reviewing: Approaches to Research Synthesis encompasses the evolution of the review literature, taxonomy of review literature, uses and users of reviews, the process of preparing reviews, assessment of review quality and impact, the impact of information technology on the preparation of reviews, and research opportunities for information science related to reviews and reviewing. In addition to providing a synthesis of prior research, this review seeks to identify gaps in the published research and to suggest possible future research directions. [ABSTRACT FROM AUTHOR]

Classifying papers according to the fields of knowledge is critical to clearly understand the dynamics of scientific (sub)fields, their leading questions, and trends. Most studies rely on journal categories defined by popular databases such as WoS or Scopus, but some experts find that those categories may not correctly map the existing subfields nor identify the subfield of a specific article. This study addresses the classification problem using data from each paper (Abstract, Title, Keywords, and the KeyWords Plus) and the help of experts to identify the existing subfields and journals exclusive of each subfield. These "exclusive journals" are critical to obtain, through a pattern detection procedure that uses machine learning techniques (from software NVivo), a list of the frequent terms that are specific to each subfield. With that list of terms and with the help of optimization procedures, we can identify to which subfield each paper most likely belongs. This study can contribute to support scientific policy‐makers, funding, and research institutions—via more accurate academic performance evaluations—, to support editors in their tasks to redefine the scopes of journals, and to support popular databases in their processes of refining categories. [ABSTRACT FROM AUTHOR]

This article explores the factors influencing the future citations of conference papers. We concentrated on the explanatory power of early attention on conference papers for citations collected from Google Scholar and Scopus. The early attention data includes users' online activities in a conference support system: CN3. Bookmarks from the bibliographic management system, Citeulike, were used as a collateral source of early attention. To examine the chronological contributions of 13 factors on citations, a multiple sequential regression analysis was conducted for three timepoints of the publication cycle—paper submission, time of conferences, and months after conferences. Our results illustrate that online readers' early attention of Citeulike bookmarks were found to have the most influence on the future impact of the conference papers. The early attention records from CN3 made noteworthy improvements to explaining both the Google and Scopus citations as well. We also found that the type of papers the number of papers presented at a conference, and the best article award records were significant factors influencing future citations. However, the magnitude of the effects made by online readers' early attention from both sources appears to be larger than these three traditional factors. [ABSTRACT FROM AUTHOR]

A model is presented of the manifestation of the birth and development of a scientific specialty in a collection of journal papers. The proposed model, Cumulative Advantage by Paper with Exemplars (CAPE) is an adaptation of Price's cumulative advantage model (D. Price, 1976). Two modifications are made: (a) references are cited in groups by paper, and (b) the model accounts for the generation of highly cited exemplar references immediately after the birth of the specialty. This simple growth process mimics many characteristic features of real collections of papers, including the structure of the paper-to-reference matrix, the reference-per-paper distribution, the paper-per-reference distribution, the bibliographic coupling distribution, the cocitation distribution, the bibliographic coupling clustering coefficient distribution, and the temporal distribution of exemplar references. The model yields a great deal of insight into the process that produces the connectedness and clustering of a collection of articles and references. Two examples are presented and successfully modeled: a collection of 131 articles on MEMS RF (microelectromechnical systems radio frequency) switches, and a collection of 901 articles on the subject of complex networks. [ABSTRACT FROM AUTHOR]

As a follow-up to the highly cited authors list published by Thomson Reuters in June 2014, we analyzed the top 1% most frequently cited papers published between 2002 and 2012 included in the Web of Science ( WoS) subject category 'Information Science & Library Science.' In all, 798 authors contributed to 305 top 1% publications; these authors were employed at 275 institutions. The authors at Harvard University contributed the largest number of papers, when the addresses are whole-number counted. However, Leiden University leads the ranking if fractional counting is used. Twenty-three of the 798 authors were also listed as most highly cited authors by Thomson Reuters in June 2014 (). Twelve of these 23 authors were involved in publishing 4 or more of the 305 papers under study. Analysis of coauthorship relations among the 798 highly cited scientists shows that coauthorships are based on common interests in a specific topic. Three topics were important between 2002 and 2012: (a) collection and exploitation of information in clinical practices; (b) use of the Internet in public communication and commerce; and (c) scientometrics. [ABSTRACT FROM AUTHOR]

Compared to previous studies that generally detect scientific breakthroughs based on citation patterns, this article proposes a knowledge entity‐based disruption indicator by quantifying the change of knowledge directly created and inspired by scientific breakthroughs to their evolutionary trajectories. Two groups of analytic units, including MeSH terms and their co‐occurrences, are employed independently by the indicator to measure the change of knowledge. The effectiveness of the proposed indicators was evaluated against the four datasets of scientific breakthroughs derived from four recognition trials. In terms of identifying scientific breakthroughs, the proposed disruption indicator based on MeSH co‐occurrences outperforms that based on MeSH terms and three earlier citation‐based disruption indicators. It is also shown that in our indicator, measuring the change of knowledge inspired by the focal paper in its evolutionary trajectory is a larger contributor than measuring the change created by the focal paper. Our study not only offers empirical insights into conceptual understanding of scientific breakthroughs but also provides practical disruption indicator for scientists and science management agencies searching for valuable research. [ABSTRACT FROM AUTHOR]

Peer review is essential for advancing scientific research, but there are long‐standing concerns that authors' prestige or other characteristics can bias reviewers. Double‐blind peer review has been proposed as a way to reduce reviewer bias, but the evidence for its effectiveness is limited and mixed. Here, we examine the effects of double‐blind peer review by analyzing the review files of 5,027 papers submitted to a top computer science conference that changed its reviewing format from single‐ to double‐blind in 2018. First, we find that the scores given to the most prestigious authors significantly decreased after switching to double‐blind review. However, because many of these papers were above the threshold for acceptance, the change did not affect paper acceptance significantly. Second, the inter‐reviewer disagreement increased significantly in the double‐blind format. Third, papers rejected in the single‐blind format are cited more than those rejected under double‐blind, suggesting that double‐blind review better excludes poorer quality papers. Lastly, an apparently unrelated change in the rating scale from 10 to 4 points likely reduced prestige bias significantly such that papers' acceptance was affected. These results support the effectiveness of double‐blind review in reducing biases, while opening new research directions on the impact of peer‐review formats. [ABSTRACT FROM AUTHOR]

The article presents information on the use of a type of matrix to analyze citations of scientific papers. Scientific papers have a series of citations belonging to the previous literature. It is possible to investigate the internal connections among papers in a group, whose papers are frequently cited by themselves, by examining their citations. The main purpose of this study is to investigate the measurements by which the internal connections of a group of scientific papers are measured. The techniques of using the type of matrix is further developed to carry out the analysis of a certain type of group of scientific papers.

There is increasing demand for a filtering system for scientific papers that can be utilized by researchers in business organizations. In conventional paper filtering systems, relevant papers are selected without sufficiently representing the researcher's interest structure, and thus the accuracy of filtering is not satisfactory. The purpose of this paper is to solve this problem and to propose a method of providing a structured representation of the researcher's interests by clustering papers of interest to the researcher. The similarity of newly appearing papers to the structured representation is calculated and the result of filtering is presented with rankings, resulting in both accuracy and convenience of use. With three researchers as the participants, an experimental evaluation of the filtering was performed by an N-fold cross-validation test, using 2 years of papers from proceedings journals in information science. An accuracy of 44% to 69% was achieved, 9.6% to 17.9% higher than the results of a filtering system without clustering used as the baseline. © 2006 Wiley Periodicals, Inc. Syst Comp Jpn, 37(12): 1–16, 2006; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/scj.20664 [ABSTRACT FROM AUTHOR]

In a conventional library the librarian eliminates individual differences in concepts in queries and maintains the reliability of information. In a digital library, however, the goal is to not involve people, and so queries which eliminate precision problems in query results are in demand as a replacement for the librarian. In current retrieval systems, precision is improved by using a hierarchical method such as synonym notation processing. In methods based on word systems, processing to handle the inadequate expression of the intention of a query or the semantic interpretation of a query are insufficient, and information retrieval from a more hierarchical perspective is needed. In this paper the authors propose a retrieval method using a hierarchical perspective based on a system of concepts, and not a retrieval method based on conventional word systems. By adopting the EDR electronic dictionary as a thesaurus and using concept information, queries and words which appear in papers are not processed merely as character strings, but are used in queries with consideration for the meaning of the words. The authors' method can estimate imprecise information in the intentions of queries and the meaning hidden in words, allowing for appropriate query results to be provided to users without the cooperation of a librarian. © 2001 Scripta Technica, Syst Comp Jpn, 32(8): 40–52, 2001 [ABSTRACT FROM AUTHOR]

This study compares the 45 “Best Paper” award articles with nonaward articles published in the Journal of Association for Information Science and Technology (JASIST) to observe the differences in citations. The results show that, in most cases, the citations of the award articles are more numerous than the median, belonging to the Top‐50% stratum. Only 15.6% of the award articles have the status of being the most‐cited article of the year in which the article was published; 24.4% belong to the Top‐5% stratum of the publication year; 44.4% belong to the Top‐10% stratum of the publication year; and 73.3% belong to the Top‐25% stratum of the publication year. Surprisingly, from 2000 to 2012, none of the award articles made it to the Top‐10% stratum, apart from the year 2004; the least‐cited award article received only three citations during a 5‐year period. The results show a wide range of citations among the Best JASIST Papers. This study also observes that the number of articles changed little from 1969 to 1995 but grew rapidly from 1996 to 2012. Suggestions for possible ways to better meet the challenges of the journal's growth in size and scope in selecting award articles are provided. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

With the wide application of keyphrases in many Information Retrieval (IR) and Natural Language Processing (NLP) tasks, automatic keyphrase prediction has been emerging. However, these statistically important phrases are contributing increasingly less to the related tasks because the end‐to‐end learning mechanism enables models to learn the important semantic information of the text directly. Similarly, keyphrases are of little help for readers to quickly grasp the paper's main idea because the relationship between the keyphrase and the paper is not explicit to readers. Therefore, we propose to generate keyphrases with specific functions for readers to bridge the semantic gap between them and the information producers, and verify the effectiveness of the keyphrase function for assisting users' comprehension with a user experiment. A controllable keyphrase generation framework (the CKPG) that uses the keyphrase function as a control code to generate categorized keyphrases is proposed and implemented based on Transformer, BART, and T5, respectively. For the Computer Science domain, the Macro‐avgs of P@5, R@5, and F1@5 on the Paper with Code dataset are up to 0.680, 0.535, and 0.558, respectively. Our experimental results indicate the effectiveness of the CKPG models. [ABSTRACT FROM AUTHOR]

Gaining a rapid overview of an emerging scientific topic, sometimes called research fronts, is an increasingly common task due to the growing amount of interdisciplinary collaboration. Visual overviews that show temporal patterns of paper publication and citation links among papers can help researchers and analysts to see the rate of growth of topics, identify key papers, and understand influences across subdisciplines. This article applies a novel network-visualization tool based on meaningful layouts of nodes to present research fronts and show citation links that indicate influences across research fronts. To demonstrate the value of two-dimensional layouts with multiple regions and user control of link visibility, we conducted a design-oriented, preliminary case study with 6 domain experts over a 4-month period. The main benefits were being able (a) to easily identify key papers and see the increasing number of papers within a research front, and (b) to quickly see the strength and direction of influence across related research fronts. [ABSTRACT FROM AUTHOR]

Although it is commonly expected that the citation context of a reference is likely to provide more detailed and direct information about the nature of a citation, few studies in the literature have specifically addressed the extent to which the information in different parts of a scientific publication differs. Do abstracts tend to use conceptually broader terms than sentences in a citation context in the body of a publication? In this article, we propose a method to analyze and compare latent topics in scientific publications, in particular, from abstracts of papers that cited a target reference and from sentences that cited the target reference. We conducted an experiment and applied topical modeling techniques to full-text papers in eight biomedicine journals. Topics derived from the two sources are compared in terms of their similarities and broad-narrow relationships defined based on information entropy. The results show that abstracts and citation contexts are characterized by distinct sets of topics with moderate overlaps. Furthermore, the results confirm that topics from abstracts of citing papers have broader terms than topics from citation contexts formed by citing sentences. The method and the findings could be used to enhance and extend the current methodologies for research evaluation and citation evaluation. [ABSTRACT FROM AUTHOR]

A central issue in evaluative bibliometrics is the characterization of the citation distribution of papers in the scientific literature. Here, we perform a large-scale empirical analysis of journals from every field in Thomson Reuters' Web of Science database. We find that only 30 of the 2,184 journals have citation distributions that are inconsistent with a discrete lognormal distribution at the rejection threshold that controls the false discovery rate at 0.05. We find that large, multidisciplinary journals are over-represented in this set of 30 journals, leading us to conclude that, within a discipline, citation distributions are lognormal. Our results strongly suggest that the discrete lognormal distribution is a globally accurate model for the distribution of “eventual impact” of scientific papers published in single-discipline journal in a single year that is removed sufficiently from the present date. [ABSTRACT FROM AUTHOR]

A network is constructed by nodes and links, thus the node degree and the link strength appear as underlying quantities in network analysis. While the power-law distribution of node degrees is verified as a basic feature of numerous real networks, we investigate whether the link strengths follow the power-law distribution in weighted networks. After testing 12 different paper cocitation networks with 2 methods, fitting in double-log scales and the Kolmogorov-Smirnov test ( K- S test), we observe that, in most cases, the link strengths also follow the approximate power-law distribution. The results suggest that the power-law type distribution could emerge not only in nodes and informational entities, but also in links and informational connections. [ABSTRACT FROM AUTHOR]

The journal structure in the China Scientific and Technical Papers and Citations Database (CSTPCD) is analyzed from three perspectives: the database level, the specialty level, and the institutional level (i.e., university journals vs. journals issued by the Chinese Academy of Sciences). The results are compared with those for (Chinese) journals included in the Science Citation Index (SCI). The frequency of journal–journal citation relations in the CSTPCD is an order of magnitude lower than in the SCI. Chinese journals, especially high-quality journals, prefer to cite international journals rather than domestic ones; however, Chinese journals do not get an equivalent reception from their international counterparts. The international visibility of Chinese journals is low, but varies among fields of science. Journals of the Chinese Academy of Sciences have a better reception in the international scientific community than university journals. [ABSTRACT FROM AUTHOR]

Four cases, illustrated by four examples, of duplicate or highly related publications can be distinguished and are analyzed here using citation data obtained from the Science Citation Index (SCI): (1) publication by different authors in the same journal; (2) the same author(s) publishing in different journals; (3) publication by different authors in different journals; (4) the same author(s) publishing highly related papers simultaneously in the same journal, often as part of a series of papers. Example 1, illustrating case 1, is an occurrence of highly related publications in mechanistic organic chemistry. Example 2, from analytical organic chemistry, contains elements of cases 2 and 3. Example 3, dealing solely with case 3, discusses two time-delayed publications from analytical biochemistry, which were highlighted by Garfield several times in the past to show how the SCI could be utilized to avoid duplicate publication. Example 4, derived from synthetic organic chemistry (total syntheses of taxol), contains elements of cases 1, 3, and 4 and, to a lesser extent, case 2. The citation records of the highly related or duplicate publications can deviate considerably from the journal impact factors; this was observed in three of the four examples relating to cases 2, 3, and 4. The examples suggest that citation of a paper may depend significantly on the journal in which it is published. As an indicator of this dependence, the journals in which the papers used in the present examples appeared were examined. Other factors such as key words in the paper title may also play a role. [ABSTRACT FROM AUTHOR]

Coauthorship prediction applies predictive analytics to bibliographic data to predict authors who are highly likely to be coauthors. In this study, we propose an approach for coauthorship prediction based on bibliographic network embedding through a graph‐based bibliographic data model that can be used to model common bibliographic data, including papers, terms, sources, authors, departments, research interests, universities, and countries. A real‐world dataset released by AMiner that includes more than 2 million papers, 8 million citations, and 1.7 million authors were integrated into a large bibliographic network using the proposed bibliographic data model. Translation‐based methods were applied to the entities and relationships to generate their low‐dimensional embeddings while preserving their connectivity information in the original bibliographic network. We applied machine learning algorithms to embeddings that represent the coauthorship relationships of the two authors and achieved high prediction results. The reference model, which is the combination of a network embedding size of 100, the most basic translation‐based method, and a gradient boosting method achieved an F1 score of 0.9 and even higher scores are obtainable with different embedding sizes and more advanced embedding methods. Thus, the strengths of the proposed approach lie in its customizable components under a unified framework. [ABSTRACT FROM AUTHOR]

A linkage similarity measure which takes into account both the bibliographic coupling of document and their cocitations (both cited and citing papers) produced improved document retrieval over a measure based only on bibliographic coupling The test collection consisted of 1712 papers whose relevance to specific queries had been judged by users. To evaluate the effect of using cocitation data, we calculated for each query two measures of similarity between each relevant paper and every other paper retrieved. Papers were then sorted by the similarity measures, producing two ordered lists. We then compared the resulting predictions of relevance, partial relevance, and non. relevance to the user's evaluations of the same papers. Over- all, the change from the bibliographic coupling measure to the linkage similarity measure, representing the introduction of cocitation data, resulted in better retrieval performance. [ABSTRACT FROM AUTHOR]

Frequencies of the number of references per paper were obtained for the information science literature represented by ail papers cited in Information Science Abstracts, volumes 1-6 11966-1971). Almost one-third of the papers had no references. Half of them had four references or fewer; two-thirds had eight or fewer references. The ratio of papers without references to those with references was proposed as a measure of the scholarly status of a field. On this basis, information science was found to be less scholarly than pharmacy. [ABSTRACT FROM AUTHOR]

This paper describes ePaper, a research prototype system of a personalized newspaper on a mobile reading device. The ePaper aggregates content (i.e., news items) from various news providers, classifies the news items according to concepts from a news domain ontology, and delivers an electronic newspaper to each subscribed user (reader). The system personalizes the content of the newspaper according to the user's profiles and preferences by applying ontological content-based and collaborative filtering algorithms. The user's profile is updated implicitly and dynamically, based on the tracking of their reading. Beyond personalization, the ePaper can also provide the user with a “standard edition” of a selected newspaper, as well as browsing capabilities in a repository of news items. The layout of the newspaper is adapted to the specifications of the reading device and to the user's preferences. In this overview paper, we highlight the main research challenges involved in the development of ePaper and describe how we addressed them. [ABSTRACT FROM AUTHOR]

Are computer science papers extended after they are published? We have surveyed 200 computer science publications, 100 journal articles, and 100 conference papers, using self-citations to identify potential and actual continuations. We are interested in determining the proportion of papers that do indeed continue, how and when the continuation takes place, and whether any distinctions are found between the journal and conference populations. Despite the implicit assumption of a research line behind each paper, manifest in the ubiquitous 'future research' notes that close many of them, we find that more than 70% of the papers are never continued. [ABSTRACT FROM AUTHOR]

Main path analysis is a popular method for extracting the scientific backbone from the citation network of a research domain. Existing approaches ignored the semantic relationships between the citing and cited publications, resulting in several adverse issues, in terms of coherence of main paths and coverage of significant studies. This paper advocated the semantic main path network analysis approach to alleviate these issues based on citation function analysis. A wide variety of SciBERT‐based deep learning models were designed for identifying citation functions. Semantic citation networks were built by either including important citations, for example, extension, motivation, usage and similarity, or excluding incidental citations like background and future work. Semantic main path network was built by merging the top‐K main paths extracted from various time slices of semantic citation network. In addition, a three‐way framework was proposed for the quantitative evaluation of main path analysis results. Both qualitative and quantitative analysis on three research areas of computational linguistics demonstrated that, compared to semantics‐agnostic counterparts, different types of semantic main path networks provide complementary views of scientific knowledge flows. Combining them together, we obtained a more precise and comprehensive picture of domain evolution and uncover more coherent development pathways between scientific ideas. [ABSTRACT FROM AUTHOR]

International collaborative papers are increasingly common in journals of many disciplines. These types of papers are often cited more frequently. To identify the coauthorship trends within Library and Information Science (LIS), this study analyzed 7,489 papers published in six leading publications (ARIST, IP&M, JAMIA, JASIST, MISQ, and Scientometrics) over the last three decades. Logistic regression tested the relationships between citations received and seven factors: authorship type, author's subregion, country income level, publication year, number of authors, document type, and journal title. The main authorship type since 1995 was national collaboration. It was also the dominant type for all publications studied except ARIST, and for all regions except Africa. For citation counts, the logistic regression analysis found all seven factors were significant. Papers that included international collaboration, Northern European authors, and authors in high-income nations had higher odds of being cited more. Papers from East Asia, Southeast Asia, and Southern Europe had lower odds than North American papers. As discussed in the bibliometric literature, Merton's Matthew Effect sheds light on the differential citation counts based on the authors' subregion. This researcher proposes geographies of invisible colleagues and a geographic scope effect to further investigate the relationships between author geographic affiliation and citation impact. [ABSTRACT FROM AUTHOR]

Novel ideas often experience resistance from incumbent forces. While evidence of the bias against novelty has been widely identified in science, there is still a lack of large‐scale quantitative work to study this problem occurring in the prepublication process of manuscripts. This paper examines the association between manuscript novelty and handling time of publication based on 778,345 articles in 1,159 journals indexed by PubMed. Measuring the novelty as the extent to which manuscripts disrupt existing knowledge, we found systematic evidence that higher novelty is associated with longer handling time. Matching and fixed‐effect models were adopted to confirm the statistical significance of this pattern. Moreover, submissions from prestigious authors and institutions have the advantage of shorter handling time, but this advantage is diminishing as manuscript novelty increases. In addition, we found longer handling time is negatively related to the impact of manuscripts, while the relationships between novelty and 3‐ and 5‐year citations are U‐shape. This study expands the existing knowledge of the novelty bias by examining its existence in the prepublication process of manuscripts. [ABSTRACT FROM AUTHOR]

The ACL Anthology is a large collection of research papers in computational linguistics. Citation data were obtained using text extraction from a collection of PDF files with significant manual postprocessing performed to clean up the results. Manual annotation of the references was then performed to complete the citation network. We analyzed the networks of paper citations, author citations, and author collaborations in an attempt to identify the most central papers and authors. The analysis includes general network statistics, Page Rank, metrics across publication years and venues, the impact factor and h-index, as well as other measures. [ABSTRACT FROM AUTHOR]

Hirsch's (2005) h index of scholarly output has generated substantial interest and wide acceptance because of its apparent ability to quantify scholarly impact simply and accurately. We show that the excitement surrounding h is premature for three reasons: h stagnates with increasing scientific age; it is highly dependent on publication quantity; and it is highly dependent on field-specific citation rates. Thus, it is not useful for comparing scholars across disciplines. We propose the scholarly “index of quality and productivity” (IQp) as an alternative to h. The new index takes into account a scholar's total impact and also corrects for field-specific citation rates, scholarly productivity, and scientific age. The IQp accurately predicts group membership on a common metric, as tested on a sample of 80 scholars from three populations: (a) Nobel winners in physics (n = 10), chemistry (n = 10), medicine (n = 10), and economics (n = 10), and towering psychologists (n = 10); and scholars who have made more modest contributions to science including randomly selected (b) fellows (n = 15) and (c) members (n = 15) of the Society of Industrial and Organizational Psychology. The IQp also correlates better with expert ratings of greatness than does the h index. [ABSTRACT FROM AUTHOR]