Previous Citation Research Articles

Using Explainable AI to Understand Team Formation and Team Impact

ABSTRACTThe citation of scientific papers is considered a simple and direct indicator of papers' impact. This paper predicts papers' citations through team‐related variables, team composition, and team structure. Team composition includes team size, male/female dominance, academia/industry collaboration, unique race number, and unique country number. Team structures are made up of team power level and team power hierarchy. Team members' previous citation number, H‐index, previous collaborators, career age, and previous paper numbers are a proxy of team power. We calculated the mean value and Gini coefficient to represent team power level (the collective team capability) and team power hierarchy (the vertical difference of power distribution within a team). Taking 1,675,035 CS teams in the DBLP dataset, we trained the XGBoost model to predict high/low citation. Our model has reached 0.71 in AUC and 70.45% in accuracy rate. Utilizing Explainable AI method SHAP to evaluate features' relative importance in predicting team citation categories, we found that team structure plays a more critical role than team composition in predicting team citation. High team power level, flat team power structure, diverse race background, large team, collaboration with industry, and male‐dominated teams can bring higher team citations. Our project can provide insights into how to form the best scientific teams and maximize team impact from team composition and team structure.

Use of the Self- and Family Management Framework in quantitative studies

BackgroundThe Self- and Family Management Framework (SFMF) identifies factors and outcomes of patient and family management of chronic illness. In a previous citation analysis, we reported the frequency and nature of use of the SFMF. PurposeWe conducted a sub-analysis of quantitative articles in the citation analysis to examine testing of relationships depicted in the SFMF. MethodsWe analyzed study purposes, independent and dependent variables, study implications, and text that referred to the SFMF in 40 articles. FindingsThe SFMF has been used largely to explore factors affecting SFM behaviors, focusing on patient versus family self-management. Independent variables included all categories of facilitators/barriers specified in the SFMF. Dependent variables included all SFMF components (Facilitators/Barriers, Processes, Proximal and Distal Outcomes). Racial/ethnic diversity was limited among study participants. Study implications pertained mostly to psychosocial outcomes. Most studies referred to the SFMF generally. DiscussionFindings will contribute to revision of the SFMF.

Continued use of retracted papers: Temporal trends in citations and (lack of) awareness of retractions shown in citation contexts in biomedicine.

We present the first database-wide study on the citation contexts of retracted papers, which covers 7,813 retracted papers indexed in PubMed, 169,434 citations collected from iCite, and 48,134 citation contexts identified from the XML version of the PubMed Central Open Access Subset. Compared with previous citation studies that focused on comparing citation counts using two time frames (i.e., preretraction and postretraction), our analyses show the longitudinal trends of citations to retracted papers in the past 60 years (1960–2020). Our temporal analyses show that retracted papers continued to be cited, but that old retracted papers stopped being cited as time progressed. Analysis of the text progression of pre- and postretraction citation contexts shows that retraction did not change the way the retracted papers were cited. Furthermore, among the 13,252 postretraction citation contexts, only 722 (5.4%) citation contexts acknowledged the retraction. In these 722 citation contexts, the retracted papers were most commonly cited as related work or as an example of problematic science. Our findings deepen the understanding of why retraction does not stop citation and demonstrate that the vast majority of postretraction citations in biomedicine do not document the retraction.

The 100 Most-Cited Articles in Liposuction: A Bibliometric Analysis.

Liposuction is one of the most common cosmetic surgical procedures performed worldwide. Despite previous citation analyses in plastic surgery, the most-cited works in liposuction have not yet been qualitatively or quantitatively appraised. We hypothesized that use of validated outcome measures and levels of evidence would be low among these articles. Thus, we performed a bibliometric analysis aiming to comprehensively review the most-cited liposuction literature, evaluating characteristics and quality of the top 100 articles. The 100 most-cited articles in liposuction were identified on Web of Science, across all available journals and years (1950-2020). Study details, including the citation count, main subject, and outcome measures, were extracted from each article by 2 independent reviewers. The level of evidence of each study was also assessed. The 100 most-cited articles in liposuction were cited by a total of 4809 articles. Citations per article ranged from 602 to 45 (mean, 92). Most articles were level of evidence 4 (n = 33) or 5 (n = 35), representative of the large number of case series, expert-opinion articles, and narrative reviews. Ten articles achieved level of evidence 3, 22 articles achieved level of evidence 2, and none reached level 1. The main subject was operative technique in 63 articles, followed by outcomes in 32 articles. Five articles assessed the metabolic effects of liposuction. Only 1 article used a validated objective cosmetic outcome measure, and none used validated patient-reported outcome measures. This analysis provides an overview of the top cited liposuction literature. Overall, level of evidence was low, and no articles achieved the highest level of evidence. Improving the quality of literature requires prioritization of better-designed studies and incorporation of validated outcome measures, which will increase patient satisfaction and ensure provision of excellent, reproducible clinical care.

Gated relational stacked denoising autoencoder with localized author embedding for global citation recommendation

Citation recommendation is an effective and efficient way to facilitate authors finding desired references. This paper presents a novel neural network based model, called gated relational probabilistic stacked denoising autoencoder with localized author (GRSLA) embedding, for global citation recommendation task. Our model is comprised of two modules with different neural network architecture. For each citing and cited papers, we use a gated paper embedding module, which is extended from probabilistic stacked denoising autoencoder (PSDAE) by adding gated units, to obtain their paper vectors. The added gated units are able to utilize text information of cited paper to refine the vector representation of citing paper in multiple semantic levels. For an author in papers, we first apply topic model to obtain his/her semantic neighbors, and then use a localized author embedding (LAE) module to excavate author vector representation from semantic and explicit neighbors. Unlike most graph convolutional network (GCN) based methods, the LAE module is able to avoid computing global Laplacian in whole graph by taking limited neighbors. Moreover, the LAE module can also be stacked to absorb more neighbors, which makes our model have high extendibility. Based on the generation process of GRSLA, we also derive a learning algorithm of our model by maximum a posteriori (MAP) estimation. We conduct experiments on the AAN, DBLP and CORD-19 datasets, and the results show that GRSLA model works well than previous global citation recommendation methods.

Apionidae of the Francesco Vitale collection (Insecta: Coleoptera)

The Francesco Vitale Sicilian collection of Apionidae is critically revised. Hoplopodapion poupillieri (Wencker, 1864), Ischnopterapion (Ischnopterapion) aeneomicans nothum (Desbrochers des Loges, 1897) and Pseudoprotapion dumeei (A. Hoffmann, 1957) are recorded for the first time from Sicily and Italy; Omphalapion laevigatum (Paykull, 1792) is recorded for the first time from Sicily, whereas the presence on the island of Aizobius sedi (Germar, 1818) and Acentrotypus brunnipes (Boheman, 1839), until now considered doubtful, is confirmed. All species quoted from Sicily by Vitale are listed and commented. In addition, the previous citation of Ceratapion (Echinostroma) basicorne (Illiger, 1807) from Cyprus is attributed to Ceratapion (Echinostroma) curtii (Wagner, 1920) and C. curtii is recorded for the first time from this country. Brief comments on our current knowledge of the apionid fauna of Sicily are also included.

The Top 50 Most-Cited Papers in the Diagnosis and Management of SLAP Tears

PurposeTo identify and characterize the top 50 most-cited articles regarding SLAP tears.MethodsReferencing the methodology of previous citation analyses, varying Boolean searches were performed using the Web of Science database and the search terms yielding the greatest number of results was used. The top 50 most-cited articles were identified and the following data points were gathered from each article: author, institution, country of origin, year of publication, publishing journal, level of evidence, and citation density.ResultsThe total number of citations was 7834, with a median of 106 citations. The top 50 list was largely composed of diagnostic level I, II, and III studies (5, 7, and 8 total publications, respectively) and therapeutic level III (6 publications) or level IV (10 publications). Most articles originated from the United States (40). In total, 19 of the top 50 most-cited articles were published in the American Journal of Sports Medicine, followed by Arthroscopy (15) and the Journal of Bone and Joint Surgery (5).ConclusionsOur analysis demonstrated a correlation with earlier publications being cited more frequently than recent studies. Importantly, the current study found that therapeutic studies in the most cited list were largely level III or level IV evidence. This makes the management of SLAP tears seem anecdotal, with little in the way of high-impact level I or level II therapeutic studies. We must reconsider our current understanding of SLAP tears and their management with more studies that demonstrate a clearer treatment algorithm for these common injuries of the shoulder.Clinical RelevanceGiven the complexity of SLAP tears, this list of the most-cited articles can provide a reference point to better guide practice, resident education, and future areas of orthopaedic research.

Is invasion science moving towards agreed standards? The influence of selected frameworks

The need to understand and manage biological invasions has driven the development of frameworks to circumscribe, classify, and elucidate aspects of the phenomenon. But how influential have these frameworks really been? To test this, we evaluated the impact of a pathway classification framework, a framework focussing on the introduction-naturalisation-invasion continuum, and two papers that outline an impact classification framework. We analysed how these framework papers are cited and by whom, conducted a survey to determine why people have cited the frameworks, and explored the degree to which the frameworks are implemented. The four papers outlining these frameworks are amongst the most-cited in their respective journals, are highly regarded in the field, and are already seen as citation classics (although citations are overwhelmingly within the field of invasion science). The number of citations to the frameworks has increased over time, and, while a significant proportion of these are self-citations (20–40%), this rate is decreasing. The frameworks were cited by studies conducted and authored by researchers from across the world. However, relative to a previous citation analysis of invasion science as a whole, the frameworks are particularly used in Europe and South Africa and less so in North America. There is an increasing number of examples of uptake into invasion policy and management (e.g., the pathway classification framework has been adapted and adopted into EU legislation and CBD targets, and the impact classification framework has been adopted by the IUCN). However, we found that few of the citing papers (6–8%) specifically implemented or interrogated the frameworks; roughly half of all citations might be viewed as frivolous (“citation fluff”); there were several clear cases of erroneous citation; and some survey respondents felt that they have not been rigorously tested yet. Although our analyses suggest that invasion science is moving towards a more systematic and standardised approach to recording invasions and their impacts, it appears that the proposed standards are still not applied consistently. For this to be achieved, we argue that frameworks in invasion science need to be revised or adapted to particular contexts in response to the needs and experiences of users (e.g., so they are relevant to pathologists, plant ecologists, and practitioners), the standards should be easier to apply in practice (e.g., through the development of guidelines for management), and there should be incentives for their usage (e.g., recognition for completing an EICAT assessment).

The Most Frequently Cited Topics in Urban Planning Scholarship

Analyses of faculty citation activity usually focus on counts as a function of author characteristics, such as rank, gender, previous citation levels, and other factors influencing productivity and career path. Citation analyses of publications consider aspects, such as the number of authors, author reputation, author order, length of the title, methodology, and impact factors of the publication. While publication topics or discipline is considered important factors, they are more difficult to analyze, and therefore, performed less frequently. This article attempts to do that for the field of urban planning. Urban planning is multi-disciplinary and includes consideration of social, economic, technological, environmental, and political systems that shape human settlement patterns. It has been suspected that some topics are more “popular” and have larger audiences, therefore, are cited more often. Using nearly 15,000 urban planning publications, this article presents an analysis of topics to assess which are cited most frequently. The classification of publications was performed using a Support Vector Machine (SVM), a machine learning (ML) approach to text classification, using citation data from Google Scholar. The citation levels for the resulting categories are analyzed and discussed.

Top 100 most cited articles in orthopaedic surgery: An update

IntroductionAs the research landscape evolves, we sought to investigate the current most cited Orthopaedic Surgery articles and compare these to previously cited articles. MethodsWeb of Science database screened orthopaedic journal articles with comparison to previous data using multivariate regression analysis. ResultsRate of citations increased 172% within the last decade. Previous rank and citations within the last decade had a greater effect on contemporary rank (p = 0.084, p = 0.002, respectively). ConclusionTrends in investigative research can shift by the next decade. Previous citation rank and citations in last decade contributed most to current rank.

An evaluation of Web of Science, Scopus and Google Scholar citations in operations management

Purpose Ongoing deliberation about how research productivity should be measured is exacerbated by extensive disparity between the number of citations for scholarly works reported by commercial academic search engines and Google Scholar (GS), the premier web crawling service for discovering research citations. Disparities identified in citation comparison studies have also led to disagreement about the value of the higher number of citations for social sciences and business scholarly articles consistently reported by GS. The purpose of this paper is to extend previous database citation comparison studies by manually analyzing a sample of unique GS citations to a leading operations management journal (i.e. citations found only in GS and not the commercial search engines) to reveal just where these additional citations are coming from. Design/methodology/approach In addition to comparing citation counts for the three databases, unique GS citation data for the sample of journal articles was manually captured and reviewed. The authors’ approach provides a much more in-depth examination of the provenance of GS citations than is found in previous studies. Findings The findings suggest that concerns about the value of unique GS citations may not be warranted since the document types for the unique GS citing documents identified in the analysis are dominated by familiar scholarly formats. Predominantly authentic and validated journal publications, dissertations, conference papers, and book and book chapters accounted for the large majority of the unique GS citations analyzed. Practical implications The study lends further credence to contentions that the use of citations reported in GS is appropriate for evaluating research impact in disciplines where other formats beyond the English-language journal article are valued. Originality/value Developing a more informed understanding of the provenance of unique GS citations in the authors’ field is important because many scholars not only aspire to publish in elite journals with high impact factors based on citation counts provided by commercial databases to demonstrate quality, but also report the larger number of citations for their publications that are reported by GS to demonstrate impact. The in-depth manual analysis suggests that GS provides a more nuanced and comprehensive representation of research impact and international scope than the commercial databases.

Emerging trends and new developments in information science: a document co-citation analysis (2009–2016)

Characterizing the structure of knowledge, the evolution of research topics, and the emergence of topics has always been an important part of information science (IS). Our previous scientometric review of IS provided a snapshot of this fast-growing field up to the end of 2008. This new study aims to identify emerging trends and new developments appearing in the subsequent 7574 articles published in 10 IS journals between 2009 and 2016, including 20,960 references. The results of a document co-citation analysis show great changes in the research topics in the IS domain. The positions of certain core topics found in the previous study, namely, information retrieval, webometrics, and citation behavior, have been replaced by scientometric indicators (H-index), citation analysis (citation performance and bibliometrics), scientific collaboration, and information behavior in the most recent period of 2009–2016. Dual-map overlays of journals show that the knowledge base of IS research has shifted considerably since 2010, with emerging topics including scientific evaluation indicators, altmetrics, science mapping and visualization, bibliometrics, citation analysis, and scientific collaboration.

A LSTM Based Model for Personalized Context-Aware Citation Recommendation

The rapid growth of scientific papers makes it difficult to find relevant and appropriate citations. Context-aware citation recommendation aims to overcome this problem by providing a list of scientific papers given a short passage of text. In this paper, we propose a long–short-term memory (LSTM)-based model for context-aware citation recommendation, which first learns the distributed representations of the citation contexts and the scientific papers separately based on LSTM, and then measures the relevance based on the learned distributed representation of citation contexts and the scientific papers. Finally, the scientific papers with high relevance scores are selected as the recommendation list. In particular, we try to incorporate author information, venue information, and content information in scientific paper distributed vector representation. Furthermore, we integrate author information of the given context in citation context distributed vector representation. Thus, the proposed model makes personalized context-aware citation recommendation possible, which is a new issue that few papers addressed in the past. When conducting experiments on the ACL Anthology Network and DBLP data sets, the results demonstrate the proposed LSTM-based model for context-aware citation recommendation is able to achieve considerable improvement over previous context-aware citation recommendation approaches. The personalized recommendation approach is also competitive with the non-personalized recommendation approach.

A evolução da citação na Revista Portuguesa de Medicina Geral e Familiar: de 2000-2008 para 2009-2015

Objetive: To measure the bibliographic citation of articles previously published in the Portuguese Journal of General Practice and compare these results with previous citation values. Study design: Observational, transversal and descriptive study. Place: Faculty of Medicine, University of Coimbra, Portugal. Population: All issues of the Portuguese Journal of General Practice published between 2009 and 2015 (including). Methods: Systematic review of the total number of bibliographic references, number of bibliographic references in articles published in Portugal and number of bibliographic references in articles published in this journal and available on its website. Comparison with previously published results. Results: Total references were 12.0±11.2 in published articles, 2.4±3.0 in articles published in Portuguese journals and 0.6±1.2 in articles published in this journal. The growth dynamics with respect to previous results are negative for the journal’s references (-33.3), for Portuguese references (-14.3), and for total references (-28.6). Conclusions: The citation values found are low and with negative growth dynamics in relation to those previously published.

The top 100 cited neurorehabilitation papers.

Neurorehabilitation covers a large range of disorders, assessment approaches and treatment methods. There have been previous citation analyses of rehabilitation and of its subfields. However, there has never been a comprehensive citation analysis in neurorehabilitation. The present study reports findings from a citation analysis of the top 100 most cited neurorehabilitation papers to describe the research trends in the field. A de-novo keyword search of papers indexed in the Web of Science Core Collection database yielded 52,581 papers. A candidate pool of the 200 most-cited papers published between 2005 and 2016 was reviewed by the clinician authors. The papers in the top 100 deemed to be irrelevant were discarded and replaced by the most highly-cited articles in the second tier deemed to be clinically relevant. The most frequently cited neurorehablitation papers appeared in Stroke, Movement Disorders, and Neurology. Papers tended to focus on treatments, especially for stroke. Authorship trends suggest that top cited papers result from group endeavors, with 90% of the papers involving a collaboration among 3 or more authors. Treatment studies, often focused on stroke, appear to have the highest impact in the field of neurorehabilitation.

Determinants of citation impact: A comparative analysis of the Global South versus the Global North

The impact of the scientific output produced by different nations in different fields varies extensively. In this article, we apply bibliometric and econometric analysis to study how citation impact varies across countries. This paper differs from previous research in that a cross-section model is put forward to account for such variation. A special focus is given to the Global South, as countries in this group have been converging with the Global North recently. We find that previous citation impact, level of international collaboration and total publications in a specific scientific field are important determinants of citation impact among all nations. However, specialization in particular scientific fields seems significantly more important in the Global South than in the Global North. These findings imply that most lower- and middle-income countries would better concentrate their resources in generating higher critical masses in specific fields, in addition to pursuing long-lasting international collaboration partnerships, as these actions may lead to higher impact research.

Academy of Management Journal, 1958–2014: a citation analysis

This paper provides a citation network analysis of publications from the Academy of Management Journal, one of the key US-based journals in the field of Management. Our analysis covers all publications in the journal from 1958---2014. This represents the entire history of the journal until the arbitrary cut-off point of our study. The paper analyses the most published authors, most cited articles, most cited authors, top institutions, and the nationalities of authors that are most represented in the journal. 2304 articles containing 114,550 references were taken from the primary data source, the Web of Science™. An analysis of 114,550 citations was carried out using the Web of Science™ online analytics tool and Excel®. Gephi™, a data visualisation and manipulation software, was used to provide a visual representation of the citation networks. Results indicate that the most published authors within AMJ throughout the journal's history are Ivancevich, Golembiewski and Hambrick. The three most cited authors within AMJ are Pfeffer, Porter and Thompson. The single most cited article is Pfeffer and Salancik's 1978 article The external control of organizations: a resource dependence perspective. A keyword analysis revealed that the most important terms used in the journal's history were `Performance', `Organization' and `Work'. Results from this paper extend our previous citation analyses of key journals in the discipline of Higher Education to a new discipline--the field of Management. The paper provides evidence of how visual analyses can help to represent the citation geography of a journal over time.

Measuring production and scholarly use of National Center for Health Statistics publications: a citation analysis in US government information

This study falls under a broad research agenda for measurement of U.S. government information with a focus on federal statistics and data. It examines patterns in both production and scholarly use of National Center for Health Statistics (NCHS) publications, using a fixed set of NCHS publications as the benchmark. Of 563 publications issued between January 1, 2010 and August 31, 2015, 168 (29.9 %) were cited at least once between January 1, 2010 and November 1, 2015. There were a total 20,550 cites to NCHS publications with an average 122 cites per publication. Fifty NCHS publications were cited at least 100 times, with Prevalence of Obesity in the United States, 2009---2010 (1669 cites); deaths: final data for 2009 (927 cites); and births: final data for 2007 (809 cites) as the top 3 cited publications. Overall, summary statistics for obesity, birth, and mortality were the most highly cited publications. This study is consistent with a previous citation analysis of federal statistics in identifying mortality as a predominant topic for production and use of NCHS publications. It also highlights several topics, including obesity, that were cited with high frequency compared to their lower publishing coverage. Future studies under this research agenda will investigate non-scholarly uses of government information, and remain focused on government statistics and data. Given trends in open government data, big data, and analytics, government statistics and data has been widely adopted in community activism and data journalism, but such use has yet to be analyzed indepth.

A business citation analysis: A local follow-up study

ABSTRACTTo determine whether the library was meeting the needs of business faculty in the School of Business Administration at the University of Mississippi, a citation analysis of business faculty publications was conducted as a follow-up to a previous citation analysis. The following data were collected: journal citations by age, publication, publisher, and Library of Congress Classification; book citations by age and Library of Congress Classification; a total was kept for all other non-journal citations. The results were analyzed for each department: Finance, Management, Management Information Systems, and Marketing.

An automatic method for extracting citations from Google Books

Recent studies have shown that counting citations from books can help scholarly impact assessment and that Google Books (GB) is a useful source of such citation counts, despite its lack of a public citation index. Searching GB for citations produces approximate matches, however, and so its raw results need time‐consuming human filtering. In response, this article introduces a method to automatically remove false and irrelevant matches from GB citation searches in addition to introducing refinements to a previous GB manual citation extraction method. The method was evaluated by manual checking of sampled GB results and comparing citations to about 14,500 monographs in the Thomson Reuters Book Citation Index (BKCI) against automatically extracted citations from GB across 24 subject areas. GB citations were 103% to 137% as numerous as BKCI citations in the humanities, except for tourism (72%) and linguistics (91%), 46% to 85% in social sciences, but only 8% to 53% in the sciences. In all cases, however, GB had substantially more citing books than did BKCI, with BKCI's results coming predominantly from journal articles. Moderate correlations between the GB and BKCI citation counts in social sciences and humanities, with most BKCI results coming from journal articles rather than books, suggests that they could measure the different aspects of impact, however.