Search

Aim: To describe the use of bibliometrics in nursing and assess their contribution to research and practice. Design: A content analysis was conducted of topics, data sources and applications of bibliometrics in nursing research articles. Methods: The study universe included 129 bibliometric articles on nursing retrieved from Scopus. A content analysis was performed to identify the purposes and topics of the articles, the sources employed to collect the data, the time frames covered, the amounts of records surveyed, and the features of the nursing literature analysed in bibliometric papers. Results: Nursing bibliometric research revolves around six key areas: global descriptions of the nursing literature, literature on specific nursing research topics, nursing education, nursing profession, nursing research using a certain framework or method, and nursing literature published in a country or region. Studies rely on three types of sources to retrieve the surveyed literature: bibliographic databases, sets of disciplinary journals and samples of documents. Bibliometrics can be employed to advance nursing research (identification of research gaps, establishment of research agendas, assessment of methodological approaches, etc.) and practice (identification of professional competences, categorisation of professional tasks, recognition of educational improvements, etc.), suggesting new avenues for researchers who aim to conduct further bibliometric research in the field. Further research is needed to assess the coverage of the nursing literature by new bibliographic data sources and to explore unaddressed topics such as gender imbalance in authorship. [ABSTRACT FROM AUTHOR]

Between 1950 and 2018, the global urban population spiralled four-fold from about 0.8 billion accounting for just 30% of the total world population to 4.2 billion in 2018 with a 55.3% share in the world population. At present, it is estimated that 57.4% of the total global population is urban and is projected to cross 60% by 2030. Given the growth and scale of urbanization, this brings forth irreversible challenges, limiting land and water resources at its disposal. This makes it difficult to meet the increasing demand for affordable housing and viable infrastructure besides other challenges. In this context, the local-level planning and development approaches that have been experimented with globally are studied to understand the potential of neighbourhoods in sustainable development. The authors conducted a systematic literature review based on the SCOPUS database to identify relevant scholarly literature. The study identified the most significant associations within various domains based on phased iterations with the VOSviewer analyser tool to gain confirmatory results. The study has explored planning interventions at the neighbourhood level that would assist in sustainable development. The article concludes that there is a lot of scope for stakeholders to channel their efforts innovatively for leveraging the potential of neighbourhoods in sustainable development. [ABSTRACT FROM AUTHOR]

There has been an implied direct connection between the scholarly literature and applied practice. However, the sport and exercise psychology community is lacking an empirical account of what practitioners believe to have been the most impactful scholarly writings to their applied practice. The purpose of this study was to survey applied practitioners of their perceived most impactful scholarly writings to their professional practice. Surveys were returned from 532 participants solicited from the Association for Applied Sport Psychology membership, who were asked to identify their perceived most impactful book and journal article to their practice. Frequency statistics were calculated and presented for topic, type, title, author(s), year published, and journal. A total of 143 different books and 188 different articles across 84 different journals were reported. Implications for applied practice, teaching sport and exercise psychology, and research are presented. [ABSTRACT FROM AUTHOR]

Classifying scientific publications according to Field-of-Science taxonomies is of crucial importance, powering a wealth of relevant applications including Search Engines, Tools for Scientific Literature, Recommendation Systems, and Science Monitoring. Furthermore, it allows funders, publishers, scholars, companies, and other stakeholders to organize scientific literature more effectively, calculate impact indicators along Science Impact pathways and identify emerging topics that can also facilitate Science, Technology, and Innovation policy-making. As a result, existing classification schemes for scientific publications underpin a large area of research evaluation with several classification schemes currently in use. However, many existing schemes are domain-specific, comprised of few levels of granularity, and require continuous manual work, making it hard to follow the rapidly evolving landscape of science as new research topics emerge. Based on our previous work of scinobo, which incorporates metadata and graph-based publication bibliometric information to assign Field-of-Science fields to scientific publications, we propose a novel hybrid approach by further employing Neural Topic Modeling and Community Detection techniques to dynamically construct a Field-of-Science taxonomy used as the backbone in automatic publication-level Field-of-Science classifiers. Our proposed Field-of-Science taxonomy is based on the OECD fields of research and development (FORD) classification, developed in the framework of the Frascati Manual containing knowledge domains in broad (first level(L1), one-digit) and narrower (second level(L2), two-digit) levels. We create a 3-level hierarchical taxonomy by manually linking Field-of-Science fields of the sciencemetrix Journal classification to the OECD/FORD level-2 fields. To facilitate a more fine-grained analysis, we extend the aforementioned Field-of-Science taxonomy to level-4 and level-5 fields by employing a pipeline of AI techniques. We evaluate the coherence and the coverage of the Field-of-Science fields for the two additional levels based on synthesis scientific publications in two case studies, in the knowledge domains of Energy and Artificial Intelligence. Our results showcase that the proposed automatically generated Field-of-Science taxonomy captures the dynamics of the two research areas encompassing the underlying structure and the emerging scientific developments.

This paper examines the societal impact of research from the perspective of interconnected genres. Information reaches professionals outside academia through many different types of documents. Those documents often connect with scholarship by referencing academic work, mentioning professors, or publishing articles authored by scholars. Here the pattern of referencing journal articles is compared across professional genres. Such citation counts make visible societal impacts to the extent that a field engages a genre, and different genres favor different fields. Biomedical sciences are most visible in patent citation counts. News and social media most often reference medicine. Policy documents make heavy use of social science. Ulrich’s indexing of trade journals, magazines, and newspapers suggests social sciences engage heavily with the professions through trade press. However, caution is warranted when using citations to indicate societal impact. Engagement with scholarship occurs not only through referencing but also through authorship and mentions. Not all citations indicate substantive engagement, particularly in social media. Academic literature is but one of many types of sources referenced in professional genres. And scholarship engages with many genres beyond those currently indexed, most notably trade press. Nevertheless, understanding citation patterns across heterogeneous professional genres offeres a promising frontier for information sciences to provide a foundation for the analysis of scholarship’s societal impact. [ABSTRACT FROM AUTHOR]

In the age of advanced information systems powering fast-paced knowledge economies that face global societal challenges, it is no longer adequate to express scholarly information - an essential resource for modern economies - primarily as article narratives in document form. Despite being a well-established tradition in scholarly communication, PDF-based text publishing is hindering scientific progress as it buries scholarly information into non-machine-readable formats. The key objective of SKG4EOSC is to improve science productivity through development and implementation of services for text and data conversion, and production, curation, and re-use of FAIR scholarly information. This will be achieved by (1) establishing the Open Research Knowledge Graph (ORKG, orkg.org), a service operated by the SKG4EOSC coordinator, as a Hub for access to FAIR scholarly information in the EOSC; (2) lifting to EOSC of numerous and heterogeneous domain-specific research infrastructures through the ORKG Hub’s harmonized access facilities; and (3) leverage the Hub to support cross-disciplinary research and policy decisions addressing societal challenges. SKG4EOSC will pilot the devised approaches and technologies in four research domains: biodiversity crisis, precision oncology, circular processes, and human cooperation. With the aim to improve machine-based scholarly information use, SKG4EOSC addresses an important current and future need of researchers. It extends the application of the FAIR data principles to scholarly communication practices, hence a more comprehensive coverage of the entire research lifecycle. Through explicit, machine actionable provenance links between FAIR scholarly information, primary data and contextual entities, it will substantially contribute to reproducibility, validation and trust in science. The resulting advanced machine support will catalyse new discoveries in basic research and solutions in key application areas.

Background: Strategies to improve the selection of appropriate target journals may reduce delays in disseminating research results. Machine learning is increasingly used in content-based recommender algorithms to guide journal submissions for academic articles. Objective: We sought to evaluate the performance of open-source artificial intelligence to predict the impact factor or Eigenfactor score tertile using academic article abstracts. Methods: PubMed-indexed articles published between 2016 and 2021 were identified with the Medical Subject Headings (MeSH) terms "ophthalmology," "radiology," and "neurology." Journals, titles, abstracts, author lists, and MeSH terms were collected. Journal impact factor and Eigenfactor scores were sourced from the 2020 Clarivate Journal Citation Report. The journals included in the study were allocated percentile ranks based on impact factor and Eigenfactor scores, compared with other journals that released publications in the same year. All abstracts were preprocessed, which included the removal of the abstract structure, and combined with titles, authors, and MeSH terms as a single input. The input data underwent preprocessing with the inbuilt ktrain Bidirectional Encoder Representations from Transformers (BERT) preprocessing library before analysis with BERT. Before use for logistic regression and XGBoost models, the input data underwent punctuation removal, negation detection, stemming, and conversion into a term frequency-inverse document frequency array. Following this preprocessing, data were randomly split into training and testing data sets with a 3:1 train:test ratio. Models were developed to predict whether a given article would be published in a first, second, or third tertile journal (0-33rd centile, 34th-66th centile, or 67th-100th centile), as ranked either by impact factor or Eigenfactor score. BERT, XGBoost, and logistic regression models were developed on the training data set before evaluation on the hold-out test data set. The primary outcome was overall classification accuracy for the best-performing model in the prediction of accepting journal impact factor tertile. Results: There were 10,813 articles from 382 unique journals. The median impact factor and Eigenfactor score were 2.117 (IQR 1.102-2.622) and 0.00247 (IQR 0.00105-0.03), respectively. The BERT model achieved the highest impact factor tertile classification accuracy of 75.0%, followed by an accuracy of 71.6% for XGBoost and 65.4% for logistic regression. Similarly, BERT achieved the highest Eigenfactor score tertile classification accuracy of 73.6%, followed by an accuracy of 71.8% for XGBoost and 65.3% for logistic regression. Conclusions: Open-source artificial intelligence can predict the impact factor and Eigenfactor score of accepting peer-reviewed journals. Further studies are required to examine the effect on publication success and the time-to-publication of such recommender systems. [ABSTRACT FROM AUTHOR]

In 2008 Meier and Conkling first tested Google Scholar's coverage of the engineering literature against citations gathered from the Compendex database. Since that time, other studies have used the same methodology and found improvement in Google Scholar's coverage. This study uses engineering dissertations from Proquest Dissertations & Theses to create a data set of citations for the comparison of fee-based databases, Compendex and Scopus, against Google Scholar. From 1950 to 2017 Google Scholar outperformed both Compendex and Scopus in discoverability of citations in nine engineering subjects. These results have implications for collection management and information literacy program planning for librarians. [ABSTRACT FROM AUTHOR]

Since the 19th century it has been accepted by the scholarly literature as solid fact that the palatinal congregations were also regularly attended by the delegates of the collegiate chapter of (Székes)fehérvár as official testimonies. Yet research into the charter material has revealed several pieces of evidence attesting that the testimony present at the congregation and sent on mission therefrom was by no means always a member of the Fehérvár chapter. Indeed, a case-based research has demonstrated that such exceptions were far from scarce up to the end of the fourteenth century. The paper accordingly explores the causes of such cases, and analyses the practice as it had developed by the first third of the fifteenth century, seeking connections with the emergence of the chapter of Fehérvár as a place of authentication of national competence. [ABSTRACT FROM AUTHOR]

In the age of advanced information systems powering fast-paced knowledge economies that face global societal challenges, it is no longer adequate to express scholarly information - an essential resource for modern economies - primarily as article narratives in document form. Despite being a well-established tradition in scholarly communication, PDF-based text publishing is hindering scientific progress as it buries scholarly information into non-machine-readable formats. The key objective of SKG4EOSC is to improve science productivity through development and implementation of services for text and data conversion, and production, curation, and re-use of FAIR scholarly information. This will be achieved by (1) establishing the Open Research Knowledge Graph (ORKG, orkg.org), a service operated by the SKG4EOSC coordinator, as a Hub for access to FAIR scholarly information in the EOSC; (2) lifting to EOSC of numerous and heterogeneous domain- specific research infrastructures through the ORKG Hub's harmonized access facilities; and (3) leverage the Hub to support cross-disciplinary research and policy decisions addressing societal challenges. SKG4EOSC will pilot the devised approaches and technologies in four research domains: biodiversity crisis, precision oncology, circular processes, and human cooperation. With the aim to improve machine-based scholarly information use, SKG4EOSC addresses an important current and future need of researchers. It extends the application of the FAIR data principles to scholarly communication practices, hence a more comprehensive coverage of the entire research lifecycle. Through explicit, machine actionable provenance links between FAIR scholarly information, primary data and contextual entities, it will substantially contribute to reproducibility, validation and trust in science. The resulting advanced machine support will catalyse new discoveries in basic research and solutions in key application areas. [ABSTRACT FROM AUTHOR]

Scholarly literature is an immense network of activities, linked via collaborations or information propagation. Analysing such network can be leveraged by harnessing rich semantic meaning of scholarly graph. Identifying and ranking top- k influential nodes from various domains of scholarly literature using social media data are still infancy. Social networking sites like Twitter provide an opportunity to create inventive graph-based measures to identify and rank influential nodes such as scholars, articles, journal, information spreading media and academic institutions of scholarly literature. Many network-based models such as centrality measures have been proposed to identify influential nodes. The empirical annotation shows that centrality measures for finding influential nodes are high in computational complexity. In addition, notion of these measures have high variance, which signifies an influential node deviation with change in application and nature of information flows in the network. The research aims to propose an ensemble learning approach based on multimodal majority voting influence (MMMVI) to identify and weighted multimodal ensemble average influence (WMMEAI) to rank top- k influential nodes in Twitter network data set of well-known three influential nodes, that is, academic institution, scholar and journal. The empirical analysis has been accomplished to learn practicability and efficiency of the proposed approaches when compared with state-of-the-art approaches. The experimental result shows that the ensemble approach using surface learning models (SLMs) can lead to better identification and ranking of influential nodes with low computational complexity. [ABSTRACT FROM AUTHOR]

The second half of the twentieth century brought about increased international contacts between people from different origins, which led to changes in the translation studies landscape, impacting conference interpreters as well. Although interpreting scholars acknowledge that interpreters are both linguistic and intercultural mediators, at first glance, culture and intercultural mediation appear to be neglected in the domain of conference interpreting, unlike the domains of community and sign language interpreting. In this paper, I conduct an analysis on how the established professional discourse and the scholarly literature have portrayed culture and intercultural mediation in conference interpreting. The analysis reveals various conceptualizations of culture and different stances regarding intercultural mediation. Amongst them, reductionist conceptualizations of culture seem to dominate; conceptualizations that have informed the conference interpreting pedagogy. However, this state of affairs contrasts sharply with the current culturally diverse interpreting landscape, what might have an impact on practice and professional identity.

Copyright of L'Année Rabelaisienne is the property of Classiques Garnier and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Background: Predatory publishers and their associated journals have been identified as a threat to the integrity of the scientific literature. Research on the phenomenon of predatory publishing in health care remains unquantified., Purpose: To identify the characteristics of empirical studies on predatory publishing in the health care literature., Methods: A scoping review was done using PubMed/MEDLINE, CINAHL, and Scopus databases. A total of 4967 articles were initially screened; 77 articles reporting empirical findings were ultimately reviewed., Results: The 77 articles were predominantly bibliometric analyses/document analyses (n = 56). The majority were in medicine (n = 31, 40%) or were multidisciplinary (n = 26, 34%); 11 studies were in nursing. Most studies reported that articles published in predatory journals were of lower quality than those published in more reputable journals. In nursing, the research confirmed that articles in predatory journals were being cited in legitimate nursing journals, thereby spreading information that may not be credible through the literature., Conclusion: The purposes of the evaluated studies were similar: to understand the characteristics and extent of the problem of predatory publishing. Although literature about predatory publishing is abundant, empirical studies in health care are limited. The findings suggest that individual vigilance alone will not be enough to address this problem in the scholarly literature. Institutional policy and technical protections are also necessary to mitigate erosion of the scientific literature in health care., Competing Interests: Declaration of Conflicting InterestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

The 2015-2016 epidemic in the Americas caused by the Zika Virus (ZIKV) triggered a dramatic increase in the number of scholarly publications on this topic. In an effort to understand and characterize this body of literature, a bibliometric study was conducted on all articles found in PubMed that were published on ZIKV between 1952 and 2016. Study parameters included publication date, source journal, subject and category of source journal, and country of first author. The data was frequency-ranked, enabling identification of the most highly productive journal titles, subject areas, and countries. The study concluded with a comparison between the number of opinion pieces versus research articles published on ZIKV and five other emerging infectious diseases. Results of the study will be of interest to students, researchers, and librarians and information science professionals, and will serve as a baseline for subsequent analyses. [ABSTRACT FROM AUTHOR]

Classifying scientific publications according to Field-of-Science taxonomies is of crucial importance, powering a wealth of relevant applications including Search Engines, Tools for Scientific Literature, Recommendation Systems, and Science Monitoring. Furthermore, it allows funders, publishers, scholars, companies, and other stakeholders to organize scientific literature more effectively, calculate impact indicators along Science Impact pathways and identify emerging topics that can also facilitate Science, Technology, and Innovation policy-making. As a result, existing classification schemes for scientific publications underpin a large area of research evaluation with several classification schemes currently in use. However, many existing schemes are domain-specific, comprised of few levels of granularity, and require continuous manual work, making it hard to follow the rapidly evolving landscape of science as new research topics emerge. Based on our previous work of scinobo, which incorporates metadata and graph-based publication bibliometric information to assign Field-of-Science fields to scientific publications, we propose a novel hybrid approach by further employing Neural Topic Modeling and Community Detection techniques to dynamically construct a Field-of-Science taxonomy used as the backbone in automatic publication-level Field-of-Science classifiers. Our proposed Field-of-Science taxonomy is based on the OECD fields of research and development (FORD) classification, developed in the framework of the Frascati Manual containing knowledge domains in broad (first level(L1), one-digit) and narrower (second level(L2), two-digit) levels. We create a 3-level hierarchical taxonomy by manually linking Field-of-Science fields of the sciencemetrix Journal classification to the OECD/FORD level-2 fields. To facilitate a more fine-grained analysis, we extend the aforementioned Field-of-Science taxonomy to level-4 and level-5 fields by employing a pipeline of AI techniques. We evaluate the coherence and the coverage of the Field-of-Science fields for the two additional levels based on synthesis scientific publications in two case studies, in the knowledge domains of Energy and Artificial Intelligence. Our results showcase that the proposed automatically generated Field-of-Science taxonomy captures the dynamics of the two research areas encompassing the underlying structure and the emerging scientific developments., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Kotitsas, Pappas, Manola and Papageorgiou.)

In this article, the authors analyze the characteristics of the vernacular management literature in Spanish-speaking countries and examine what factors can explain the differences observed in the production of these countries. The results indicate that management research has gained ground in the last decade, as revealed by the apparition of new journals and the number of articles published. The article also shows that Spain has a different profile of published articles than that of the other countries, as its production is mostly composed of empirical-quantitative articles. Finally, a negative binomial regression model suggests that the countries' scientific output is positively associated with their level of economic development and the size of their economies. [ABSTRACT FROM PUBLISHER]

Copyright of Cahiers d'Études Africaines is the property of Editions EHESS and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This article examines the scholarly production of Spanish-speaking countries in management and organizational studies, focusing on scientific articles in peer-reviewed journals. We differentiate the production between two literature segments according to publication language. The article's goals are to analyze both segments of the academic literature and to identify what characteristics of the authors lead them to publish in a particular segment. The analysis reveals that both the researchers' human and social capital differentiate their participation in these literature segments. Most importantly, we demonstrate that, regardless of these individual characteristics, the type of higher education institution where scholars are affiliated with seems to significantly influence the segment of academic literature in which they predominantly publish. [ABSTRACT FROM AUTHOR]

Purpose – The aim of this paper is to lay out some of the more complex issues arising in the area of publication ethics. The impact of electronic publishing and electronic information is a main focus of the paper. Design/methodology/approach – The paper draws in particular upon the work of the Committee on Publication Ethics including illustrative cases discussed at the forum, guidelines and discussion documents. Findings – Three areas are highlighted to stimulate discussion around challenges of publication ethics in the digital era. These are the role of the internet in facilitating misconduct, the issue of confidentiality in publishing and how incentives in research assessments drive author behavior. Originality/value – The paper brings together a variety of issues discussed under the broader umbrella of electronic information and new technologies in publishing. [ABSTRACT FROM AUTHOR]

• This article presents a content analysis of Management International Review for the fifteen-year period 1993-2007. A total of 360 papers appearing in the journal during that period were analyzed to identify the key trends in the evolution of Management International Review. • The five major themes addressed included an examination of the nature of authorship, an identification and ranking of the most prolific authors, an evaluation of the characteristics of the articles, recognition of the most influential articles based on the number of citations they received, and an uncovering of the specific thematic areas within the published articles. • The study combines publishing productivity and citation analyses. The conclusions derived from the study are presented and some guidelines for future research provided. [ABSTRACT FROM AUTHOR]

Copyright of Agricultural Information Worldwide is the property of International Association of Agricultural Information Specialists (IAALD) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The article presents information on a case study that covers a two-year period during which the principal authors, a social work faculty member and a senior librarian, collaborated to discover why undergraduate seniors were having difficulty locating fifteen required scholarly journal articles needed to begin to write their individual, topic-specific senior thesis. The senior coordinating seminar (capstone course) has as its major assignment the writing of a guided comprehensive paper that integrates the nine curriculum content areas required by the Council on Social Work Education. This article serves both as the University's required senior comprehensive assessment and as an outcome measure of the student's social work content knowledge. Most importantly, successful completion of the assignment demonstrates student competency in critical thinking and commitment to scholarship. Students are asked to select a topic relevant to social work, and within the first two weeks of the course locate a minimum of fifteen scholarly journal articles germane to their topic.

Aim To question the efficacy of 'gold' open access to published articles. Background Open access is unrestricted access to academic, theoretical and research literature that is scholarly and peer-reviewed. Two models of open access exist: 'gold' and 'green'. Gold open access provides everyone with access to articles during all stages of publication, with processing charges paid by the author(s). Green open access involves placing an already published article into a repository to provide unrestricted access, with processing charges incurred by the publisher. Data sources This is a discussion paper. Review methods An exploration of the relative benefits and drawbacks of the 'gold' and 'green' open access systems. Discussion Green open access is a more economic and efficient means of granting open access to scholarly literature but a large number of researchers select gold open access journals as their first choices for manuscript submissions. This paper questions the efficacy of gold open access models and presents an examination of green open access models to encourage nurse researchers to consider this approach. Conclusion In the current academic environment, with increased pressures to publish and low funding success rates, it is difficult to understand why gold open access still exists. Green open access enhances the visibility of an academic's work, as increased downloads of articles tend to lead to increased citations. Implications for research/practice Green open access is the cheaper option, as well as the most beneficial choice, for universities that want to provide unrestricted access to all literature at minimal risk. [ABSTRACT FROM AUTHOR]