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To more reasonably allocate a paper's credit, this article argues that both a paper's authors and references contribute to a given paper. Accordingly, we quantitatively represent the proportion of contributions from each author and reference to a paper. A paper's credit can be allocated among its authors and references based on their contributions. All papers carry innate credit because of publication. If cited, they also carry external credit from the citing papers. The proportion of a paper's credit allocated to references can be regarded as a credit output and serves as an input for these references. In this scenario, only the credit assigned to a paper's authors remains as the paper's deserved credit. The credit of papers can be transferred in a direction opposite that of knowledge diffusion. Via this method, the estimate of an individual reference's contribution incorporates content‐based citation analysis, a promising method to differentiate different citations. A paper's deserved credit represents the contribution of the paper's authors to the scientific community via the new knowledge they provide in the paper. Therefore, it is rational to evaluate papers according to their deserved credit, not the credit they carry. [ABSTRACT FROM AUTHOR]

When writing a research paper, the author has to select information to include in the paper to support various arguments. The information has to be organized and synthesized into a coherent whole through relationships and information structures. There is hardly any research on the information structure of research papers, and how information structure supports rhetorical and argument structures. Thus, this study is focused on information organization in the Abstract and Introduction sections of sociology research papers, analyzing the information structure of research objective, question, hypothesis, and result statements. The study is limited to research papers reporting research that investigated cause–effect relations between two concepts. Two semantic frames were developed to specify the types of information associated with cause–effect and comparison relations, and used as coding schemes to annotate the text for different information types. Six link patterns between the two frames were identified—showing how comparisons are used to support the claim that the cause‐effect relation is valid. This study demonstrated how semantic frames can be incorporated in discourse analysis to identify deep structures underlying the argument structure. The results carry implications for the knowledge representation of academic research in knowledge graphs, for semantic relation extraction, and teaching of academic writing. [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]

When writing a research paper, the author has to select information to include in the paper to support various arguments. The information has to be organized and synthesized into a coherent whole through relationships and information structures. There is hardly any research on the information structure of research papers, and how information structure supports rhetorical and argument structures. Thus, this study is focused on information organization in the Abstract and Introduction sections of sociology research papers, analyzing the information structure of research objective, question, hypothesis, and result statements. The study is limited to research papers reporting research that investigated cause–effect relations between two concepts. Two semantic frames were developed to specify the types of information associated with cause–effect and comparison relations, and used as coding schemes to annotate the text for different information types. Six link patterns between the two frames were identified—showing how comparisons are used to support the claim that the cause‐effect relation is valid. This study demonstrated how semantic frames can be incorporated in discourse analysis to identify deep structures underlying the argument structure. The results carry implications for the knowledge representation of academic research in knowledge graphs, for semantic relation extraction, and teaching of academic writing. [ABSTRACT FROM AUTHOR]

The BRICS countries ( Brazil, Russia, India, China, and South Africa) are notable for their increasing participation in science and technology. The governments of these countries have been boosting their investments in research and development to become part of the group of nations doing research at a world-class level. This study investigates the development of the BRICS countries in the domain of top-cited papers (top 10% and 1% most frequently cited papers) between 1990 and 2010. To assess the extent to which these countries have become important players at the top level, we compare the BRICS countries with the top-performing countries worldwide. As the analyses of the (annual) growth rates show, with the exception of Russia, the BRICS countries have increased their output in terms of most frequently cited papers at a higher rate than the top-cited countries worldwide. By way of additional analysis, we generate coauthorship networks among authors of highly cited papers for 4 time points to view changes in BRICS participation (1995, 2000, 2005, and 2010). Here, the results show that all BRICS countries succeeded in becoming part of this network, whereby the Chinese collaboration activities focus on the US. [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]

This article provides the first historical analysis of the relationship between collaboration and scientific impact using three indicators of collaboration (number of authors, number of addresses, and number of countries) derived from articles published between 1900 and 2011. The results demonstrate that an increase in the number of authors leads to an increase in impact, from the beginning of the last century onward, and that this is not due simply to self-citations. A similar trend is also observed for the number of addresses and number of countries represented in the byline of an article. However, the constant inflation of collaboration since 1900 has resulted in diminishing citation returns: Larger and more diverse (in terms of institutional and country affiliation) teams are necessary to realize higher impact. The article concludes with a discussion of the potential causes of the impact gain in citations of collaborative papers. [ABSTRACT FROM AUTHOR]

The aim of this paper is to extend our knowledge about the power-law relationship between citation-based performance and coauthorship patterns in papers in the natural sciences. We analyzed 829,924 articles that received 16,490,346 citations. The number of articles published through coauthorship accounts for 89%. The citation-based performance and coauthorship patterns exhibit a power-law correlation with a scaling exponent of 1.20 ± 0.07. Citations to a subfield's research articles tended to increase 2.1.20 or 2.30 times each time it doubled the number of coauthored papers. The scaling exponent for the power-law relationship for single-authored papers was 0.85 ± 0.11. The citations to a subfield's single-authored research articles increased 2.0.85 or 1.89 times each time the research area doubled the number of single-authored papers. The Matthew Effect is stronger for coauthored papers than for single-authored. In fact, with a scaling exponent <1.0 the impact of single-authored papers exhibits a cumulative disadvantage or inverse Matthew Effect. [ABSTRACT FROM AUTHOR]

In many scientific fields, the order of coauthors on a paper conveys information about each individual's contribution to a piece of joint work. We argue that in prior network analyses of coauthorship networks, the information on ordering has been insufficiently considered because ties between authors are typically symmetrized. This is basically the same as assuming that each coauthor has contributed equally to a paper. We introduce a solution to this problem by adopting a coauthorship credit allocation model proposed by Kim and Diesner (2014), which in its core conceptualizes coauthoring as a directed, weighted, and self-looped network. We test and validate our application of the adopted framework based on a sample data of 861 authors who have published in the journal Psychometrika. The results suggest that this novel sociometric approach can complement traditional measures based on undirected networks and expand insights into coauthoring patterns such as the hierarchy of collaboration among scholars. As another form of validation, we also show how our approach accurately detects prominent scholars in the Psychometric Society affiliated with the journal. [ABSTRACT FROM AUTHOR]

The h-index was devised to represent a scholar's contributions to his field with respect to the number of publications and citations. It does not, however, take into consideration the scholar's position in the authorship list. I recommend a new supplementary index to score academics, representing the relative contribution to the papers with impact, be reported alongside the h-index. I call this index the AP-index, and it is simply defined as the average position in which an academic appears in authorship lists, on articles that factor in to that academic's h-index. [ABSTRACT FROM AUTHOR]

The vast amount of scientific publications available online makes it easier for students and researchers to reuse text from other authors and makes it harder for checking the originality of a given text. Reusing text without crediting the original authors is considered plagiarism. A number of studies have reported the prevalence of plagiarism in academia. As a consequence, numerous institutions and researchers are dedicated to devising systems to automate the process of checking for plagiarism. This work focuses on the problem of detecting text reuse in scientific papers. The contributions of this paper are twofold: (a) we survey the existing approaches for plagiarism detection based on content, based on content and structure, and based on citations and references; and (b) we compare content and citation-based approaches with the goal of evaluating whether they are complementary and if their combination can improve the quality of the detection. We carry out experiments with real data sets of scientific papers and concluded that a combination of the methods can be beneficial. [ABSTRACT FROM AUTHOR]

The paper reports a longitudinal analysis of the topical and methodological development of Library and Information Science (LIS). Its focus is on the effects of researchers' disciplines on these developments. The study extends an earlier cross‐sectional study (Vakkari et al., Journal of the Association for Information Science and Technology, 2022a, 73, 1706–1722) by a coordinated dataset representing a content analysis of articles published in 31 scholarly LIS journals in 1995, 2005, and 2015. It is novel in its coverage of authors' disciplines, topical and methodological aspects in a coordinated dataset spanning two decades thus allowing trend analysis. The findings include a shrinking trend in the share of LIS from 67 to 36% while Computer Science, and Business and Economics increase their share from 9 and 6% to 21 and 16%, respectively. The earlier cross‐sectional study (Vakkari et al., Journal of the Association for Information Science and Technology, 2022a, 73, 1706–1722) for the year 2015 identified three topical clusters of LIS research, focusing on topical subfields, methodologies, and contributing disciplines. Correspondence analysis confirms their existence already in 1995 and traces their development through the decades. The contributing disciplines infuse their concepts, research questions, and approaches to LIS and may also subsume vital parts of LIS in their own structures of knowledge production. [ABSTRACT FROM AUTHOR]

This study investigates scholars' citation behaviors from a fine‐grained perspective. Specifically, each scholarly citation is considered multidimensional rather than logically unidimensional (i.e., present or absent). Thirty million articles from PubMed were accessed for use in empirical research, in which a total of 15 interpretable features of scholarly citations were constructed and grouped into three main categories. Each category corresponds to one aspect of the reasons and motivations behind scholars' citation decision‐making during academic writing. Using about 500,000 pairs of actual and randomly generated scholarly citations, a series of Random Forest‐based classification experiments were conducted to quantitatively evaluate the correlation between each constructed citation feature and citation decisions made by scholars. Our experimental results indicate that citation proximity is the category most relevant to scholars' citation decision‐making, followed by citation authority and citation inertia. However, big‐name scholars whose h‐indexes rank among the top 1% exhibit a unique pattern of citation behaviors—their citation decision‐making correlates most closely with citation inertia, with the correlation nearly three times as strong as that of their ordinary counterparts. Hopefully, the empirical findings presented in this paper can bring us closer to characterizing and understanding the complex process of generating scholarly citations in academia. [ABSTRACT FROM AUTHOR]

Scientific collaboration is essential in solving problems and breeding innovation. Coauthor network analysis has been utilized to study scholars' collaborations for a long time, but these studies have not simultaneously taken different collaboration features into consideration. In this paper, we present a systematic approach to analyze the differences in possibilities that two authors will cooperate as seen fromthe effects of homophily, transitivity, and preferential attachment. Exponential random graph models (ERGMs) are applied in this research. We find that different types of publications one author has written play diverse roles in his/her collaborations. An author's tendency to form new collaborations with her/his coauthors' collaborators is strong, where the more coauthors one author had before, the more new collaborators he/she will attract. We demonstrate that considering the authors' attributes and homophily effects as well as the transitivity and preferential attachment effects of the coauthorship network in which they are embedded helps us gain a comprehensive understanding of scientific collaboration. [ABSTRACT FROM AUTHOR]

In a range of scientific coauthorship networks, transitions emerge in degree distribution, in the correlation between degree and local clustering coefficient, etc. The existence of those transitions could be regarded because of the diversity in collaboration behaviors of scientific fields. A growing geometric hypergraph built on a cluster of concentric circles is proposed to model two specific collaboration behaviors, namely the behaviors of research team leaders and those of the other team members. The model successfully predicts the transitions, as well as many common features of coauthorship networks. Particularly, it realizes a process of deriving the complex “scale‐free” property from the simple “yes/no” decisions. Moreover, it provides a reasonable explanation for the emergence of transitions with the difference of collaboration behaviors between leaders and other members. The difference emerges in the evolution of research teams, which synthetically addresses several specific factors of generating collaborations, namely the communications between research teams, academic impacts and homophily of authors. [ABSTRACT FROM AUTHOR]

Diffusion channels are critical to determining the adoption scale, which leads to the ultimate impact of an innovation. The aim of this study is to develop an integrative understanding of the impact of two diffusion channels (i.e., broadcasting vs. virality) on innovation adoption. Using citations of a series of classic algorithms and the time series of co‐authorship as the footprints of their diffusion trajectories, we propose a novel method to analyze the intertwining relationships between broadcasting and virality in the innovation diffusion process. Our findings show that broadcasting and virality have similar diffusion power, but play different roles across diffusion stages. Broadcasting is more powerful in the early stages but may be gradually caught up or even surpassed by virality in the later period. Meanwhile, diffusion speed in virality is significantly faster than broadcasting and members from virality channels tend to adopt the same innovation repetitively. [ABSTRACT FROM AUTHOR]

This article explores the cultural characteristics of three open access ( OA)-friendly disciplines (physics, economics, and clinical medicine) and the ways in which those characteristics influence perceptions, motivations, and behaviors toward green OA. The empirical data are taken from two online surveys of European authors. Taking a domain analytic approach, the analysis draws on Becher and Trowler's (2001) and Whitley's (2000) theories to gain a deeper understanding of why OA repositories ( OAR) play a particularly important role in the chosen disciplines. The surveys provided a unique opportunity to compare perceptions, motivations, and behaviors of researchers at the discipline level with the parent metadiscipline. It should be noted that participants were not drawn from a stratified sample of all the different subdisciplines that constitute each discipline, and therefore the generalizability of the findings to the discipline may be limited. The differential role of informal and formal communication in each of the three disciplines has shaped green OA practices. For physicists and economists, preprints are an essential feature of their respective OAR landscapes, whereas for clinical medics final published articles have a central role. In comparing the disciplines with their parent metadisciplines there were some notable similarities/differences, which have methodological implications for studying research cultures. [ABSTRACT FROM AUTHOR]

This study explores the usefulness of full-text retrieval in assessing obliteration by incorporation ( OBI) by comparing patterns of OBI and citation substitution across economics, management, and psychology for two concept catch phrases-bounded rationality and satisficing. Searches using each term are conducted in JSTOR and in selected additional full-text journal sources from over the years 1987-2011. Two measures of OBI are used, one simply tallying the presence or absence of references to Simon's oeuvre (strict OBI) linked to the catch phrase and one counting only papers lacking any embedded reference as evidence of obliteration (lenient OBI). By either measure, OBI existed but varied across subject area, time period, and catch phrase. Economics had the highest strict OBI (82%) and lenient OBI (43%) for bounded rationality and the highest strict OBI (64%) for satisficing; all 3 subject areas were essentially tied for lenient OBI at about 30%. Sixty-two percent of the articles for bounded rationality-psychology were retrieved only because the catch phrase occurred in a title in the article bibliography. OBI research can benefit from full-text searching; the main tradeoff is more detailed and nuanced evidence concerning OBI existence and trends versus increased noise in the retrieval. [ABSTRACT FROM AUTHOR]

This study explores the use of PubPeer by the scholarly community, to understand the issues discussed in an online journal club, the disciplines most commented on, and the characteristics of the most prolific users. A sample of 39,985 posts about 24,779 publications were extracted from PubPeer in 2019 and 2020. These comments were divided into seven categories according to their degree of seriousness (Positive review, Critical review, Lack of information, Honest errors, Methodological flaws, Publishing fraud, and Manipulation). The results show that more than two‐thirds of comments are posted to report some type of misconduct, mainly about image manipulation. These comments generate most discussion and take longer to be posted. By discipline, Health Sciences and Life Sciences are the most discussed research areas. The results also reveal "super commenters," users who access the platform to systematically review publications. The study ends by discussing how various disciplines use the site for different purposes. [ABSTRACT FROM AUTHOR]

Authorship attribution attempts to reveal the authors of documents. In recent years, research in this field has grown rapidly. However, the performance of state‐of‐the‐art methods is heavily affected when text of known authorship and texts under investigation differ in topic and/or genre. So far, it is not clear how to quantify the personal style of authors in a way that is not affected by topic shifts or genre variations. In this paper, a set of text distortion methods are used attempting to mask topic‐related information. These methods transform the input texts into a more topic‐neutral form while maintaining the structure of documents associated with the personal style of the author. Using a controlled corpus that includes a fine‐grained range of topics and genres it is demonstrated how the proposed approach can be combined with existing authorship attribution methods to enhance their performance in very challenging tasks, especially in cross‐topic attribution. We also examine cross‐genre attribution and the most challenging, yet realistic, cross‐topic‐and‐genre attribution scenarios and show how the proposed techniques should be tuned to enhance performance in these tasks. Finally, we demonstrate that there are important differences in attribution effectiveness when either conversational genres, nonconversational genres, or a mix of them are considered. [ABSTRACT FROM AUTHOR]

Decomposing a document written by more than one author into sentences based on authorship is of great significance due to the increasing demand for plagiarism detection, forensic analysis, civil law (i.e., disputed copyright issues), and intelligence issues that involve disputed anonymous documents. Among existing studies for document decomposition, some were limited by specific languages, according to topics or restricted to a document of two authors, and their accuracies have big room for improvement. In this paper, we consider the contextual correlation hidden among sentences and propose an algorithm for Sequential and Unsupervised Decomposition of a Multi‐Author Document (SUDMAD) written in any language, disregarding topics, through the construction of a Hidden Markov Model (HMM) reflecting the authors' writing styles. To build and learn such a model, an unsupervised, statistical approach is first proposed to estimate the initial values of HMM parameters of a preliminary model, which does not require the availability of any information of author's or document's context other than how many authors contributed to writing the document. To further boost the performance of this approach, a boosted HMM learning procedure is proposed next, where the initial classification results are used to create labeled training data to learn a more accurate HMM. Moreover, the contextual relationship among sentences is further utilized to refine the classification results. Our proposed approach is empirically evaluated on three benchmark datasets that are widely used for authorship analysis of documents. Comparisons with recent state‐of‐the‐art approaches are also presented to demonstrate the significance of our new ideas and the superior performance of our approach. [ABSTRACT FROM AUTHOR]

This review of the literature presents an overview of the last 50 years of research on acknowledgments in the context of scholarly communication. Through qualitative coding and bibliometric methods, this meta-synthesis provides an in-depth description of acknowledgments research and reveals the five main thematic categories that emerge from this corpus of literature. Adopting a historical approach, this review shows a diversified and scattered research landscape. Despite five decades of analysis putting forward the potential value of acknowledgments as markers of scientific capital, the literature still lacks consensus as to the value and functions of acknowledgments within the reward system of science. [ABSTRACT FROM AUTHOR]

Design of peer-review support systems is shaped by the policies that define and govern the process of peer review. An important component of these are policies that deal with anonymity: The rules that govern the concealment and transparency of information related to identities of the various stakeholders (authors, reviewers, editors, and others) involved in the peer-review process. Anonymity policies have been a subject of debate for several decades within scholarly communities. Because of widespread criticism of traditional peer-review processes, a variety of new peer-review processes have emerged that manage the trade-offs between disclosure and concealment of identities in different ways. Based on an analysis of policies and guidelines for authors and reviewers provided by publication venues, we developed a framework for understanding how disclosure and concealment of identities is managed. We discuss the appropriate role of information technology and computer support for the peer-review process within that framework. [ABSTRACT FROM AUTHOR]

Brazilian scholarly output has rapidly increased, accompanied by the expansion of domestic collaborations. In this paper, we identify spatial patterns of collaboration in Brazil and measure the role of geographic proximity in determining the interaction among researchers. Using a database comprising more than one million researchers and seven million publications, we consolidated information on interregional research collaboration in terms of scientific coauthorship networks among 4,615 municipalities during the period between 1992 and 2009, which allowed us to analyze a range of data unprecedented in the literature. The effects of geographic distance on collaboration were measured for different areas by estimating spatial interaction models. The main results provide strong evidence of geographic deconcentration of collaboration in recent years, with increased participation of authors in scientifically less traditional regions, such as south and northeast Brazil. Distance remains a significant factor in determining the intensity of knowledge flow in collaboration networks in Brazil, as an increase of 100 km between two researchers reduces the probability of collaboration by an average of 16%, and there is no evidence that the effect of distance has diminished over time, although the magnitude of such effects varies among networks of different areas. [ABSTRACT FROM AUTHOR]

This paper describes and evaluates an unsupervised and effective authorship verification model called S patium-L1. As features, we suggest using the 200 most frequent terms of the disputed text (isolated words and punctuation symbols). Applying a simple distance measure and a set of impostors, we can determine whether or not the disputed text was written by the proposed author. Moreover, based on a simple rule we can define when there is enough evidence to propose an answer or when the attribution scheme is unable to make a decision with a high degree of certainty. Evaluations based on 6 test collections ( PAN CLEF 2014 evaluation campaign) indicate that S patium- L1 usually appears in the top 3 best verification systems, and on an aggregate measure, presents the best performance. The suggested strategy can be adapted without any problem to different Indo-European languages (such as English, Dutch, Spanish, and Greek) or genres (essay, novel, review, and newspaper article). [ABSTRACT FROM AUTHOR]

In several author name disambiguation studies, some ethnic name groups such as East Asian names are reported to be more difficult to disambiguate than others. This implies that disambiguation approaches might be improved if ethnic name groups are distinguished before disambiguation. We explore the potential of ethnic name partitioning by comparing performance of four machine learning algorithms trained and tested on the entire data or specifically on individual name groups. Results show that ethnicity-based name partitioning can substantially improve disambiguation performance because the individual models are better suited for their respective name group. The improvements occur across all ethnic name groups with different magnitudes. Performance gains in predicting matched name pairs outweigh losses in predicting nonmatched pairs. Feature (e.g., coauthor name) similarities of name pairs vary across ethnic name groups. Such differencesmay enable the development of ethnicity-specific feature weights to improve prediction for specific ethic name categories. These findings are observed for three labeled data with a natural distribution of problem sizes as well as one in which all ethnic name groups are controlled for the same sizes of ambiguous names. This study is expected to motive scholars to group author names based on ethnicity prior to disambiguation. [ABSTRACT FROM AUTHOR]

Scholars have often relied on name initials to resolve name ambiguities in large-scale coauthorship network research. This approach bears the risk of incorrectly merging or splitting author identities. The use of initial-based disambiguation has been justified by the assumption that such errors would not affect research findings too much. This paper tests that assumption by analyzing coauthorship networks from five academic fields-biology, computer science, nanoscience, neuroscience, and physics-and an interdisciplinary journal, PNAS. Name instances in data sets of this study were disambiguated based on heuristics gained from previous algorithmic disambiguation solutions. We use disambiguated data as a proxy of ground-truth to test the performance of three types of initial-based disambiguation. Our results show that initial-based disambiguation can misrepresent statistical properties of coauthorship networks: It deflates the number of unique authors, number of components, average shortest paths, clustering coefficient, and assortativity, while it inflates average productivity, density, average coauthor number per author, and largest component size. Also, on average, more than half of top 10 productive or collaborative authors drop off the lists. Asian names were found to account for the majority of misidentification by initial-based disambiguation due to their common surname and given name initials. [ABSTRACT FROM AUTHOR]

Collaboration is encouraged because it is believed to improve academic research, supported by indirect evidence in the form of more coauthored articles being more cited. Nevertheless, this might not reflect quality but increased self‐citations or the "audience effect": citations from increased awareness through multiple author networks. We address this with the first science wide investigation into whether author numbers associate with journal article quality, using expert peer quality judgments for 122,331 articles from the 2014–20 UK national assessment. Spearman correlations between author numbers and quality scores show moderately strong positive associations (0.2–0.4) in the health, life, and physical sciences, but weak or no positive associations in engineering and social sciences, with weak negative/positive or no associations in various arts and humanities, and a possible negative association for decision sciences. This gives the first systematic evidence that greater numbers of authors associates with higher quality journal articles in the majority of academia outside the arts and humanities, at least for the UK. Positive associations between team size and citation counts in areas with little association between team size and quality also show that audience effects or other nonquality factors account for the higher citation rates of coauthored articles in some fields. [ABSTRACT FROM AUTHOR]

In this article, we present a method to automatically build large labeled datasets for the author ambiguity problem in the academic world by leveraging the authoritative academic resources, ORCID and DOI. Using the method, we built LAGOS‐AND, two large, gold‐standard sub‐datasets for author name disambiguation (AND), of which LAGOS‐AND‐BLOCK is created for clustering‐based AND research and LAGOS‐AND‐PAIRWISE is created for classification‐based AND research. Our LAGOS‐AND datasets are substantially different from the existing ones. The initial versions of the datasets (v1.0, released in February 2021) include 7.5 M citations authored by 798 K unique authors (LAGOS‐AND‐BLOCK) and close to 1 M instances (LAGOS‐AND‐PAIRWISE). And both datasets show close similarities to the whole Microsoft Academic Graph (MAG) across validations of six facets. In building the datasets, we reveal the variation degrees of last names in three literature databases, PubMed, MAG, and Semantic Scholar, by comparing author names hosted to the authors' official last names shown on the ORCID pages. Furthermore, we evaluate several baseline disambiguation methods as well as the MAG's author IDs system on our datasets, and the evaluation helps identify several interesting findings. We hope the datasets and findings will bring new insights for future studies. The code and datasets are publicly available. [ABSTRACT FROM AUTHOR]

It is well known that, within the Latin production of written text, peculiar metric schemes were followed not only in poetic compositions, but also in many prose works. Such metric patterns were based on so‐called syllabic quantity, that is, on the length of the involved syllables, and there is substantial evidence suggesting that certain authors had a preference for certain metric patterns over others. In this research we investigate the possibility to employ syllabic quantity as a base for deriving rhythmic features for the task of computational authorship attribution of Latin prose texts. We test the impact of these features on the authorship attribution task when combined with other topic‐agnostic features. Our experiments, carried out on three different datasets using support vector machines (SVMs) show that rhythmic features based on syllabic quantity are beneficial in discriminating among Latin prose authors. [ABSTRACT FROM AUTHOR]

A time-dependent centrality metric for disciplinary coauthorship graphs, the ' Nobel number' for a discipline, is introduced. A researcher's Nobel number for a given discipline in a given year is defined as the researcher's average coauthorship distance to that discipline's Nobel laureates in that year. Plotting Nobel numbers over several decades provides a quantitative as well as visual indication of a researcher's proximity to the intuitive 'center' of a discipline as defined by recognized scientific achievement. It is shown that the Nobel number distributions for physics of several researchers both within and outside of physics are surprisingly flat over the five-decade span from 1951 to 2000. A model in which Nobel laureates are typically connected by short coauthorship paths both intergenerationally and between subdisciplines reproduces such flat Nobel number distributions. [ABSTRACT FROM AUTHOR]

Collaborator recommendation is of great significance for facilitating research collaboration. Proximities have been demonstrated to be significant factors and determinants of research collaboration. Research leadership is associated with not only the capability to integrate resources to launch and sustain the research project but also the production and academic impact of the collaboration team. However, existing studies mainly focus on social or cognitive proximity, failing to integrate critical proximities comprehensively. Besides, existing studies focus on recommending relationships among all the coauthors, ignoring leadership in research collaboration. In this article, we propose a proximity‐aware research leadership recommendation (PRLR) model to systematically integrate critical node attribute information (critical proximities) and network features to conduct research leadership recommendation by predicting the directed links in the research leadership network. PRLR integrates cognitive, geographical, and institutional proximity as node attribute information and constructs a leadership‐aware coauthorship network to preserve the research leadership information. PRLR learns the node attribute information, the local network features, and the global network features with an autoencoder model, a joint probability constraint, and an attribute‐aware skip‐gram model, respectively. Extensive experiments and ablation studies have been conducted, demonstrating that PRLR significantly outperforms the state‐of‐the‐art collaborator recommendation models in research leadership recommendation. [ABSTRACT FROM AUTHOR]

The author discusses the age of fractionalized authorship and nano publication in the 21st century that has made measuring individual contributions and divvying up credit equitably difficult. Topics discussed include how the essential character of academic authorship is changed to a quasi-industrial process and the need to revise the concept of authorship to better reflect the multi-modal nature of 21st century scholarly communication.

Efforts to combat continuing gender inequalities in academia need to be informed by evidence about where differences occur. Citations are relevant as potential evidence in appointment and promotion decisions, but it is unclear whether there have been historical gender differences in average citation impact that might explain the current shortfall of senior female academics. This study investigates the evolution of gender differences in citation impact 1996–2018 for six million articles from seven large English‐speaking nations: Australia, Canada, Ireland, Jamaica, New Zealand, UK, and the USA. The results show that a small female citation advantage has been the norm over time for all these countries except the USA, where there has been no practical difference. The female citation advantage is largest, and statistically significant in most years, for Australia and the UK. This suggests that any academic bias against citing female‐authored research cannot explain current employment inequalities. Nevertheless, comparisons using recent citation data, or avoiding it altogether, during appointments or promotion may disadvantage females in some countries by underestimating the likely greater impact of their work, especially in the long term. [ABSTRACT FROM AUTHOR]

Research collaboration is promoted by governments and research funders, but if the relative prevalence and merits of collaboration vary internationally then different national and disciplinary strategies may be needed to promote it. This study compares the team size and field normalized citation impact of research across all 27 Scopus broad fields in the 10 countries with the most journal articles indexed in Scopus 2008–2012. The results show that team size varies substantially by discipline and country, with Japan (4.2) having two‐thirds more authors per article than the United Kingdom (2.5). Solo authorship is rare in China (4%) but common in the United Kingdom (27%). While increasing team size associates with higher citation impact in almost all countries and fields, this association is much weaker in China than elsewhere. There are also field differences in the association between citation impact and collaboration. For example, larger team sizes in the Business, Management & Accounting category do not seem to associate with greater research impact, and for China and India, solo authorship associates with higher citation impact in this field. Overall, there are substantial international and field differences in the extent to which researchers collaborate and the extent to which collaboration associates with higher citation impact. [ABSTRACT FROM AUTHOR]

In author name disambiguation, author forenames are used to decide which name instances are disambiguated together and how much they are likely to refer to the same author. Despite such a crucial role of forenames, their effect on the performance of heuristic (string matching) and algorithmic disambiguation is not well understood. This study assesses the contributions of forenames in author name disambiguation using multiple labeled data sets under varying ratios and lengths of full forenames, reflecting real‐world scenarios in which an author is represented by forename variants (synonym) and some authors share the same forenames (homonym). The results show that increasing the ratios of full forenames substantially improves both heuristic and machine‐learning‐based disambiguation. Performance gains by algorithmic disambiguation are pronounced when many forenames are initialized or homonyms are prevalent. As the ratios of full forenames increase, however, they become marginal compared to those by string matching. Using a small portion of forename strings does not reduce much the performances of both heuristic and algorithmic disambiguation methods compared to using full‐length strings. These findings provide practical suggestions, such as restoring initialized forenames into a full‐string format via record linkage for improved disambiguation performances. [ABSTRACT FROM AUTHOR]

The purpose of the study was to understand if and how 2 proposed facets of self‐identity (work‐self and career‐self) and journals' perceived quality (impact, visibility, and content quality) influence and explain the intention to publish in open access (OA) or nonopen access (non‐OA) journals. This study integrates attitude and identity theory within a cross‐sectional survey design. The sample consists of about 1,600 researchers in Norway, and the data were collected via e‐mail invitation using a digital surveying tool and analyzed using structural equation modeling techniques. We determined that perceived impact‐quality increases the intention to publish non‐OA, while decreasing the intention to publish OA. Content quality is only associated with non‐OA journals. Perceived visibility increases the intention to publish OA, while the opposite effect is found for non‐OA. Career‐self salience has the strongest effect on impact‐quality, while content quality is most important when work‐self is salient. This research contributes to a deeper understanding about how perceived quality influences intention to publish in OA and non‐OA journals, and how self‐identity salience affects different facets of perceived quality in valence and strength. Findings have implications for policy development, implementation, and assessment and may contribute to improving OA adoption. [ABSTRACT FROM AUTHOR]

There is a mutual resemblance between the behavior of users of the Stack Exchange and the dynamics of the citations accumulation process in the scientific community, which enabled us to tackle the outwardly intractable problem of assessing the impact of introducing "negative" citations. Although the most frequent reason to cite an article is to highlight the connection between the 2 publications, researchers sometimes mention an earlier work to cast a negative light. While computing citation‐based scores, for instance, the h‐index, information about the reason why an article was mentioned is neglected. Therefore, it can be questioned whether these indices describe scientific achievements accurately. In this article we shed insight into the problem of "negative" citations, analyzing data from Stack Exchange and, to draw more universal conclusions, we derive an approximation of citations scores. Here we show that the quantified influence of introducing negative citations is of lesser importance and that they could be used as an indicator of where the attention of the scientific community is allocated. [ABSTRACT FROM AUTHOR]

With the significant development of China's economy and scientific activity, its scientific publication activity is experiencing a period of rapid growth. However, measuring China's research output remains a challenge because Chinese scholars may publish their research in either international or national journals, yet no bibliometric database covers both the Chinese and English scientific literature. The purpose of this study is to compare Web of Science (WoS) with a Chinese bibliometric database in terms of authors and their performance, demonstrate the extent of the overlap between the two groups of Chinese most productive authors in both international and Chinese bibliometric databases, and determine how different disciplines may affect this overlap. The results of this study indicate that Chinese bibliometric databases, or a combination of WoS and Chinese bibliometric databases, should be used to evaluate Chinese research performance except in the few disciplines in which Chinese research performance could be assessed using WoS only. [ABSTRACT FROM AUTHOR]

Authorship analysis attempts to reveal information about authors of digital documents enabling applications in digital humanities, text forensics, and cyber‐security. Author verification is a fundamental task where, given a set of texts written by a certain author, we should decide whether another text is also by that author. In this article we systematically study the usefulness of topic modeling in author verification. We examine several author verification methods that cover the main paradigms, namely, intrinsic (attempt to solve a one‐class classification task) and extrinsic (attempt to solve a binary classification task) methods as well as profile‐based (all documents of known authorship are treated cumulatively) and instance‐based (each document of known authorship is treated separately) approaches combined with well‐known topic modeling methods such as Latent Semantic Indexing (LSI) and Latent Dirichlet Allocation (LDA). We use benchmark data sets and demonstrate that LDA is better combined with extrinsic methods, while the most effective intrinsic method is based on LSI. Moreover, topic modeling seems to be particularly effective for profile‐based approaches and the performance is enhanced when latent topics are extracted by an enriched set of documents. The comparison to state‐of‐the‐art methods demonstrates the great potential of the approaches presented in this study. It is also demonstrates that even when genre‐agnostic external documents are used, the proposed extrinsic models are very competitive. [ABSTRACT FROM AUTHOR]

Researchers have different ways of deciding on the author order, and how they do it often depends on the culture of their field. Some fields are well known for using alphabetic author order, while others put a great emphasis on the meaning of the author order and place authors according to contribution. This article is the first to use mixed method to examine the extent of alphabetic author order and to examine why researchers adopt a certain author order norm in the fields of economics and political science. The article finds that alphabetic authorship has been and is the norm in economics, while some tendency towards it exists in political science. The differences in the intellectual and social organization of the fields seem to be a factor in the extent that these researchers will adopt a certain norm. Furthermore, the increasing number of authors per article and the publish‐or‐perish culture seems to put pressure on the alphabetic norm because it creates greater attention to the reputational advantages of being first‐author. [ABSTRACT FROM AUTHOR]

This research article puts forward a method to identify the national heritage of authors based on the morphology of their surnames. Most studies in the field use variants of dictionary‐based surname methods to identify ethnic communities, an approach that suffers from methodological limitations. Using the public file of ORCID (Open Researcher and Contributor ID) identifiers in 2015, we developed a surname‐based identification method and applied it to infer Russian heritage from suffix‐based morphological regularities. The method was developed conceptually and tested in an undersampled control set. Identification based on surname morphology was then complemented by using first‐name data to eliminate false‐positive results. The method achieved 98% precision and 94% recall rates—superior to most other methods that use name data. The procedure can be adapted to identify the heritage of a variety of national groups with morphologically regular naming traditions. We elaborate on how the method can be employed to overcome long‐standing limitations of using name data in bibliometric datasets. This identification method can contribute to advancing research in scientific mobility and migration, patenting by certain groups, publishing and collaboration, transnational and scientific diaspora links, and the effects of diversity on the innovative performance of organizations, regions, and countries. [ABSTRACT FROM AUTHOR]