Abstract Purpose This study takes advantage of newly released journal metrics to investigate whether local journals with more qualified boards have lower acceptance rates, based on data from 219 Turkish national journals and 2,367 editorial board members. Design/methodology/approach This study argues that journal editors can signal their scholarly quality by publishing in reputable journals. Conversely, editors publishing inside articles in affiliated national journals would send negative signals. The research predicts that high (low) quality editorial boards will conduct more (less) selective evaluation and their journals will have lower (higher) acceptance rates. Based on the publication strategy of editors, four measures of board quality are defined: Number of board inside publications per editor (INSIDER), number of board Social Sciences Citation Index publications per editor (SSCI), inside-to-SSCI article ratio (ISRA), and board citation per editor (CITATION). Predictions are tested by correlation and regression analysis. Findings Low-quality board proxies (INSIDER, ISRA) are positively, and high-quality board proxies (SSCI, CITATION) are negatively associated with acceptance rates. Further, we find that receiving a larger number of submissions, greater women representation on boards, and Web of Science and Scopus (WOSS) coverage are associated with lower acceptance rates. Acceptance rates for journals range from 12% to 91%, with an average of 54% and a median of 53%. Law journals have significantly higher average acceptance rate (68%) than other journals, while WOSS journals have the lowest (43%). Findings indicate some of the highest acceptance rates in Social Sciences literature, including competitive Business and Economics journals that traditionally have low acceptance rates. Limitations Research relies on local context to define publication strategy of editors. Findings may not be generalizable to mainstream journals and core science countries where emphasis on research quality is stronger and editorial selection is based on scientific merit. Practical implications Results offer useful insights into editorial management of national journals and allow us to make sense of local editorial practices. The importance of scientific merit for selection to national journal editorial boards is particularly highlighted for sound editorial evaluation of submitted manuscripts. Originality/value This is the first attempt to document a significant relation between acceptance rates and editorial board publication behavior.
{"title":"Editorial board publication strategy and acceptance rates in Turkish national journals","authors":"Lokman Tutuncu","doi":"10.2478/jdis-2023-0019","DOIUrl":"https://doi.org/10.2478/jdis-2023-0019","url":null,"abstract":"Abstract Purpose This study takes advantage of newly released journal metrics to investigate whether local journals with more qualified boards have lower acceptance rates, based on data from 219 Turkish national journals and 2,367 editorial board members. Design/methodology/approach This study argues that journal editors can signal their scholarly quality by publishing in reputable journals. Conversely, editors publishing inside articles in affiliated national journals would send negative signals. The research predicts that high (low) quality editorial boards will conduct more (less) selective evaluation and their journals will have lower (higher) acceptance rates. Based on the publication strategy of editors, four measures of board quality are defined: Number of board inside publications per editor (INSIDER), number of board Social Sciences Citation Index publications per editor (SSCI), inside-to-SSCI article ratio (ISRA), and board citation per editor (CITATION). Predictions are tested by correlation and regression analysis. Findings Low-quality board proxies (INSIDER, ISRA) are positively, and high-quality board proxies (SSCI, CITATION) are negatively associated with acceptance rates. Further, we find that receiving a larger number of submissions, greater women representation on boards, and Web of Science and Scopus (WOSS) coverage are associated with lower acceptance rates. Acceptance rates for journals range from 12% to 91%, with an average of 54% and a median of 53%. Law journals have significantly higher average acceptance rate (68%) than other journals, while WOSS journals have the lowest (43%). Findings indicate some of the highest acceptance rates in Social Sciences literature, including competitive Business and Economics journals that traditionally have low acceptance rates. Limitations Research relies on local context to define publication strategy of editors. Findings may not be generalizable to mainstream journals and core science countries where emphasis on research quality is stronger and editorial selection is based on scientific merit. Practical implications Results offer useful insights into editorial management of national journals and allow us to make sense of local editorial practices. The importance of scientific merit for selection to national journal editorial boards is particularly highlighted for sound editorial evaluation of submitted manuscripts. Originality/value This is the first attempt to document a significant relation between acceptance rates and editorial board publication behavior.","PeriodicalId":92237,"journal":{"name":"Journal of data and information science (Warsaw, Poland)","volume":"0 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42529544","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract Purpose Media exaggerations of health research may confuse readers’ understanding, erode public trust in science and medicine, and cause disease mismanagement. This study built artificial intelligence (AI) models to automatically identify and correct news headlines exaggerating obesity-related research findings. Design/methodology/approach We searched popular digital media outlets to collect 523 headlines exaggerating obesity-related research findings. The reasons for exaggerations include: inferring causality from observational studies, inferring human outcomes from animal research, inferring distant/end outcomes (e.g., obesity) from immediate/intermediate outcomes (e.g., calorie intake), and generalizing findings to the population from a subgroup or convenience sample. Each headline was paired with the title and abstract of the peer-reviewed journal publication covered by the news article. We drafted an exaggeration-free counterpart for each original headline and fined-tuned a BERT model to differentiate between them. We further fine-tuned three generative language models—BART, PEGASUS, and T5 to autogenerate exaggeration-free headlines based on a journal publication’s title and abstract. Model performance was evaluated using the ROUGE metrics by comparing model-generated headlines with journal publication titles. Findings The fine-tuned BERT model achieved 92.5% accuracy in differentiating between exaggeration-free and original headlines. Baseline ROUGE scores averaged 0.311 for ROUGE-1, 0.113 for ROUGE-2, 0.253 for ROUGE-L, and 0.253 ROUGE-Lsum. PEGASUS, T5, and BART all outperformed the baseline. The best-performing BART model attained 0.447 for ROUGE-1, 0.221 for ROUGE-2, 0.402 for ROUGE-L, and 0.402 for ROUGE-Lsum. Originality/value This study demonstrated the feasibility of leveraging AI to automatically identify and correct news headlines exaggerating obesity-related research findings.
{"title":"Build neural network models to identify and correct news headlines exaggerating obesity-related scientific findings","authors":"R. An, Quinlan Batcheller, Junjie Wang, Yuyi Yang","doi":"10.2478/jdis-2023-0014","DOIUrl":"https://doi.org/10.2478/jdis-2023-0014","url":null,"abstract":"Abstract Purpose Media exaggerations of health research may confuse readers’ understanding, erode public trust in science and medicine, and cause disease mismanagement. This study built artificial intelligence (AI) models to automatically identify and correct news headlines exaggerating obesity-related research findings. Design/methodology/approach We searched popular digital media outlets to collect 523 headlines exaggerating obesity-related research findings. The reasons for exaggerations include: inferring causality from observational studies, inferring human outcomes from animal research, inferring distant/end outcomes (e.g., obesity) from immediate/intermediate outcomes (e.g., calorie intake), and generalizing findings to the population from a subgroup or convenience sample. Each headline was paired with the title and abstract of the peer-reviewed journal publication covered by the news article. We drafted an exaggeration-free counterpart for each original headline and fined-tuned a BERT model to differentiate between them. We further fine-tuned three generative language models—BART, PEGASUS, and T5 to autogenerate exaggeration-free headlines based on a journal publication’s title and abstract. Model performance was evaluated using the ROUGE metrics by comparing model-generated headlines with journal publication titles. Findings The fine-tuned BERT model achieved 92.5% accuracy in differentiating between exaggeration-free and original headlines. Baseline ROUGE scores averaged 0.311 for ROUGE-1, 0.113 for ROUGE-2, 0.253 for ROUGE-L, and 0.253 ROUGE-Lsum. PEGASUS, T5, and BART all outperformed the baseline. The best-performing BART model attained 0.447 for ROUGE-1, 0.221 for ROUGE-2, 0.402 for ROUGE-L, and 0.402 for ROUGE-Lsum. Originality/value This study demonstrated the feasibility of leveraging AI to automatically identify and correct news headlines exaggerating obesity-related research findings.","PeriodicalId":92237,"journal":{"name":"Journal of data and information science (Warsaw, Poland)","volume":"8 1","pages":"88 - 97"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45890018","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract Purpose The purpose of this study is to propose an improved credit allocation method that makes the leading author of the paper more distinguishable and makes the deification more robust under malicious manipulations. Design/methodology/approach We utilize a modified Sigmoid function to handle the fat-tail distributed citation counts. We also remove the target paper in calculating the contribution of co-citations. Following previous studies, we use 30 Nobel Prize-winning papers and their citation networks based on the American Physical Society (APS) and the Microsoft Academic Graph (MAG) dataset to test the accuracy of our proposed method (NCCAS). In addition, we use 654,148 articles published in the field of computer science from 2000 to 2009 in the MAG dataset to validate the distinguishability and robustness of NCCAS. Finding Compared with the state-of-the-art methods, NCCAS gives the most accurate prediction of Nobel laureates. Furthermore, the leading author of the paper identified by NCCAS is more distinguishable compared with other co-authors. The results by NCCAS are also more robust to malicious manipulation. Finally, we perform ablation studies to show the contribution of different components in our methods. Research limitations Due to limited ground truth on the true leading author of a work, the accuracy of NCCAS and other related methods can only be tested in Nobel Physics Prize-winning papers. Practical implications NCCAS is successfully applied to a large number of publications, demonstrating its potential in analyzing the relationship between the contribution and the recognition of authors with different by-line orders. Originality/value Compared with existing methods, NCCAS not only identifies the leading author of a paper more accurately, but also makes the deification more distinguishable and more robust, providing a new tool for related studies.
{"title":"An author credit allocation method with improved distinguishability and robustness","authors":"Yang Li, Tao Jia","doi":"10.2478/jdis-2023-0016","DOIUrl":"https://doi.org/10.2478/jdis-2023-0016","url":null,"abstract":"Abstract Purpose The purpose of this study is to propose an improved credit allocation method that makes the leading author of the paper more distinguishable and makes the deification more robust under malicious manipulations. Design/methodology/approach We utilize a modified Sigmoid function to handle the fat-tail distributed citation counts. We also remove the target paper in calculating the contribution of co-citations. Following previous studies, we use 30 Nobel Prize-winning papers and their citation networks based on the American Physical Society (APS) and the Microsoft Academic Graph (MAG) dataset to test the accuracy of our proposed method (NCCAS). In addition, we use 654,148 articles published in the field of computer science from 2000 to 2009 in the MAG dataset to validate the distinguishability and robustness of NCCAS. Finding Compared with the state-of-the-art methods, NCCAS gives the most accurate prediction of Nobel laureates. Furthermore, the leading author of the paper identified by NCCAS is more distinguishable compared with other co-authors. The results by NCCAS are also more robust to malicious manipulation. Finally, we perform ablation studies to show the contribution of different components in our methods. Research limitations Due to limited ground truth on the true leading author of a work, the accuracy of NCCAS and other related methods can only be tested in Nobel Physics Prize-winning papers. Practical implications NCCAS is successfully applied to a large number of publications, demonstrating its potential in analyzing the relationship between the contribution and the recognition of authors with different by-line orders. Originality/value Compared with existing methods, NCCAS not only identifies the leading author of a paper more accurately, but also makes the deification more distinguishable and more robust, providing a new tool for related studies.","PeriodicalId":92237,"journal":{"name":"Journal of data and information science (Warsaw, Poland)","volume":"8 1","pages":"15 - 46"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47722992","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract Purpose This paper aims to investigate the differences between conference papers and journal papers in the field of computer science based on Bayesian network. Design/methodology/approach This paper investigated the differences between conference papers and journal papers in the field of computer science based on Bayesian network, a knowledge-representative framework that can model relationships among all variables in the network. We defined the variables required for Bayesian networks modeling, calculated the values of each variable based Aminer dataset (a literature data set in the field of computer science), learned the Bayesian network and derived some findings based on network inference. Findings The study found that conferences are more attractive to senior scholars, the academic impact of conference papers is slightly higher than journal papers, and it is uncertain whether conference papers are more innovative than journal papers. Research limitations The study was limited to the field of computer science and employed Aminer dataset as the sample. Further studies involving more diverse datasets and different fields could provide a more complete picture of the matter. Practical implications By demonstrating that Bayesian networks can effectively analyze issues in Scientometrics, the study offers valuable insights that may enhance researchers’ understanding of the differences between journal and conference in computer science. Originality/value Academic conferences play a crucial role in facilitating scholarly exchange and knowledge dissemination within the field of computer science. Several studies have been conducted to examine the distinctions between conference papers and journal papers in terms of various factors, such as authors, citations, h-index and others. Those studies were carried out from different (independent) perspectives, lacking a systematic examination of the connections and interactions between multiple perspectives. This paper supplements this deficiency based on Bayesian network modeling.
{"title":"Differences between journal and conference in computer science: a bibliometric view based on Bayesian network","authors":"Mingyue Sun, Mingliang Yue, Tingcan Ma","doi":"10.2478/jdis-2023-0017","DOIUrl":"https://doi.org/10.2478/jdis-2023-0017","url":null,"abstract":"Abstract Purpose This paper aims to investigate the differences between conference papers and journal papers in the field of computer science based on Bayesian network. Design/methodology/approach This paper investigated the differences between conference papers and journal papers in the field of computer science based on Bayesian network, a knowledge-representative framework that can model relationships among all variables in the network. We defined the variables required for Bayesian networks modeling, calculated the values of each variable based Aminer dataset (a literature data set in the field of computer science), learned the Bayesian network and derived some findings based on network inference. Findings The study found that conferences are more attractive to senior scholars, the academic impact of conference papers is slightly higher than journal papers, and it is uncertain whether conference papers are more innovative than journal papers. Research limitations The study was limited to the field of computer science and employed Aminer dataset as the sample. Further studies involving more diverse datasets and different fields could provide a more complete picture of the matter. Practical implications By demonstrating that Bayesian networks can effectively analyze issues in Scientometrics, the study offers valuable insights that may enhance researchers’ understanding of the differences between journal and conference in computer science. Originality/value Academic conferences play a crucial role in facilitating scholarly exchange and knowledge dissemination within the field of computer science. Several studies have been conducted to examine the distinctions between conference papers and journal papers in terms of various factors, such as authors, citations, h-index and others. Those studies were carried out from different (independent) perspectives, lacking a systematic examination of the connections and interactions between multiple perspectives. This paper supplements this deficiency based on Bayesian network modeling.","PeriodicalId":92237,"journal":{"name":"Journal of data and information science (Warsaw, Poland)","volume":"8 1","pages":"47 - 60"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47950652","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract Purpose Nowadays, public opinions during public emergencies involve not only textual contents but also contain images. However, the existing works mainly focus on textual contents and they do not provide a satisfactory accuracy of sentiment analysis, lacking the combination of multimodal contents. In this paper, we propose to combine texts and images generated in the social media to perform sentiment analysis. Design/methodology/approach We propose a Deep Multimodal Fusion Model (DMFM), which combines textual and visual sentiment analysis. We first train word2vec model on a large-scale public emergency corpus to obtain semantic-rich word vectors as the input of textual sentiment analysis. BiLSTM is employed to generate encoded textual embeddings. To fully excavate visual information from images, a modified pretrained VGG16-based sentiment analysis network is used with the best-performed fine-tuning strategy. A multimodal fusion method is implemented to fuse textual and visual embeddings completely, producing predicted labels. Findings We performed extensive experiments on Weibo and Twitter public emergency datasets, to evaluate the performance of our proposed model. Experimental results demonstrate that the DMFM provides higher accuracy compared with baseline models. The introduction of images can boost the performance of sentiment analysis during public emergencies. Research limitations In the future, we will test our model in a wider dataset. We will also consider a better way to learn the multimodal fusion information. Practical implications We build an efficient multimodal sentiment analysis model for the social media contents during public emergencies. Originality/value We consider the images posted by online users during public emergencies on social platforms. The proposed method can present a novel scope for sentiment analysis during public emergencies and provide the decision support for the government when formulating policies in public emergencies.
{"title":"Multimodal sentiment analysis for social media contents during public emergencies","authors":"Tao Fan, Hao Wang, Peng Wu, Chen Ling, Milad Taleby Ahvanooey","doi":"10.2478/jdis-2023-0012","DOIUrl":"https://doi.org/10.2478/jdis-2023-0012","url":null,"abstract":"Abstract Purpose Nowadays, public opinions during public emergencies involve not only textual contents but also contain images. However, the existing works mainly focus on textual contents and they do not provide a satisfactory accuracy of sentiment analysis, lacking the combination of multimodal contents. In this paper, we propose to combine texts and images generated in the social media to perform sentiment analysis. Design/methodology/approach We propose a Deep Multimodal Fusion Model (DMFM), which combines textual and visual sentiment analysis. We first train word2vec model on a large-scale public emergency corpus to obtain semantic-rich word vectors as the input of textual sentiment analysis. BiLSTM is employed to generate encoded textual embeddings. To fully excavate visual information from images, a modified pretrained VGG16-based sentiment analysis network is used with the best-performed fine-tuning strategy. A multimodal fusion method is implemented to fuse textual and visual embeddings completely, producing predicted labels. Findings We performed extensive experiments on Weibo and Twitter public emergency datasets, to evaluate the performance of our proposed model. Experimental results demonstrate that the DMFM provides higher accuracy compared with baseline models. The introduction of images can boost the performance of sentiment analysis during public emergencies. Research limitations In the future, we will test our model in a wider dataset. We will also consider a better way to learn the multimodal fusion information. Practical implications We build an efficient multimodal sentiment analysis model for the social media contents during public emergencies. Originality/value We consider the images posted by online users during public emergencies on social platforms. The proposed method can present a novel scope for sentiment analysis during public emergencies and provide the decision support for the government when formulating policies in public emergencies.","PeriodicalId":92237,"journal":{"name":"Journal of data and information science (Warsaw, Poland)","volume":"8 1","pages":"61 - 87"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41694711","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract It is imperative that all stakeholders within the research ecosystem take responsibility to improve research integrity and reliability of published research. Based on the unique experiences of a specialist publishing ethics and research integrity team within a major publisher, this article provides insights into the observed trends of misconduct and how those have evolved over time, and addresses key actions needed to improve the interface between researchers, funders, institutions and publishers to collectively improve research integrity on a global scale.
{"title":"Perspectives from a publishing ethics and research integrity team for required improvements","authors":"Sabina Alam, L. Wilson","doi":"10.2478/jdis-2023-0018","DOIUrl":"https://doi.org/10.2478/jdis-2023-0018","url":null,"abstract":"Abstract It is imperative that all stakeholders within the research ecosystem take responsibility to improve research integrity and reliability of published research. Based on the unique experiences of a specialist publishing ethics and research integrity team within a major publisher, this article provides insights into the observed trends of misconduct and how those have evolved over time, and addresses key actions needed to improve the interface between researchers, funders, institutions and publishers to collectively improve research integrity on a global scale.","PeriodicalId":92237,"journal":{"name":"Journal of data and information science (Warsaw, Poland)","volume":"8 1","pages":"1 - 14"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44893068","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract Cancer research is occasionally described as being in a reproducibility crisis. The cancer literature has ample papers retracted due to misconduct, including the use of paper mills, invalid authorship, or fake data. The objective of this paper was to gain an appreciation of the balance of retractions and associated retraction notices of 23 retracted Cancer Biotherapy and Radiopharmaceuticals papers associated with paper mills. By 23 March 2023, these retracted papers had already accumulated 287 citations according to Web of Science Core Collection, 253 according to Scopus, and 365 according to Google Scholar, i.e., metrically speaking, they were highly rewarded. All authors had an affiliation (71% being a hospital) in China. Most (12/21; 57%) of corresponding authors had emails with a @163.com suffix. Four of the retraction notices (i.e., 17%) explicitly indicated paper mills as a reason for retraction although, in general, the retraction notices lacked details and background that could assist readers’ understanding of the retractions.
摘要癌症研究偶尔被描述为处于再现性危机中。癌症文献中有大量论文因不当行为而被撤回，包括使用造纸厂、无效作者或伪造数据。本文的目的是了解23篇与造纸厂相关的撤回癌症生物治疗和放射性药物论文的撤回平衡和相关撤回通知。截至2023年3月23日，根据Web of Science Core Collection的数据，这些被撤回的论文已经累积了287次引用，Scopus的数据为253次，Google Scholar的数据为365次，也就是说，从度量上讲，它们获得了很高的奖励。所有作者在中国都有附属机构（71%是医院）。大多数（12/21；57%）通讯作者的电子邮件后缀为@163.com。四份撤回通知（即17%）明确指出造纸厂是撤回的原因，尽管总体而言，撤回通知缺乏有助于读者理解撤回的细节和背景。
{"title":"Assessment of retracted papers, and their retraction notices, from a cancer journal associated with “paper mills”","authors":"J. A. T. da Silva, Serhii Nazarovets","doi":"10.2478/jdis-2023-0009","DOIUrl":"https://doi.org/10.2478/jdis-2023-0009","url":null,"abstract":"Abstract Cancer research is occasionally described as being in a reproducibility crisis. The cancer literature has ample papers retracted due to misconduct, including the use of paper mills, invalid authorship, or fake data. The objective of this paper was to gain an appreciation of the balance of retractions and associated retraction notices of 23 retracted Cancer Biotherapy and Radiopharmaceuticals papers associated with paper mills. By 23 March 2023, these retracted papers had already accumulated 287 citations according to Web of Science Core Collection, 253 according to Scopus, and 365 according to Google Scholar, i.e., metrically speaking, they were highly rewarded. All authors had an affiliation (71% being a hospital) in China. Most (12/21; 57%) of corresponding authors had emails with a @163.com suffix. Four of the retraction notices (i.e., 17%) explicitly indicated paper mills as a reason for retraction although, in general, the retraction notices lacked details and background that could assist readers’ understanding of the retractions.","PeriodicalId":92237,"journal":{"name":"Journal of data and information science (Warsaw, Poland)","volume":"8 1","pages":"118 - 125"},"PeriodicalIF":0.0,"publicationDate":"2023-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41400112","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
E. Gzoyan, A. Mirzoyan, Anush Sargsyan, Mariam Yeghikyan, D. Maisano, Shushanik A. Sargsyan
Abstract Purpose Nearly 122 scientific journals are currently being published in Armenia—of which only six are indexed by WoS and/or Scopus databases. The majority of the national journals are published in the Armenian language, solely possessing abstracts written in English, although there are also English-language and multi-language journals with articles not only in Armenian but also in other foreign languages. The aim of this article is to study the visibility of the (non-indexed) national Armenian journals in the WoS database through citation analysis. In consideration of the existence of a relevant Armenian “diaspora” in the world, this article also attempts to estimate its impact in terms of citation statistics. Design/methodology/approach For this end, we have identified citations to the national/domestic Armenian journals in the WoS database in comparison with the share of citations received from “diaspora” researchers (researchers of Armenian origin born in foreign countries and those originally from Armenia who have emigrated to foreign countries). Findings Among the 116 Armenian domestic journals analyzed (not indexed by WoS), only 47 were found to be cited in WoS. Of these journals, almost 12% are citations by “diaspora” researchers, most of which concern Social Science and Humanities journals. Research limitations Although the surnames of Armenians end with -i(y)an, sometimes, the Diaspora Armenians, surnames are changed or modified or they are not ending with -i(y)an, in this case we may fail to identify them. Practical implications This study can help to build new, more deep and comprehensive relations with scientific diasporas. Originality/value This study offers a new understanding of multifaced research collaboration with scientific diasporas and their role in internationalization of domestic journals.
{"title":"International visibility of Armenian domestic journals: the role of scientific diaspora","authors":"E. Gzoyan, A. Mirzoyan, Anush Sargsyan, Mariam Yeghikyan, D. Maisano, Shushanik A. Sargsyan","doi":"10.2478/jdis-2023-0011","DOIUrl":"https://doi.org/10.2478/jdis-2023-0011","url":null,"abstract":"Abstract Purpose Nearly 122 scientific journals are currently being published in Armenia—of which only six are indexed by WoS and/or Scopus databases. The majority of the national journals are published in the Armenian language, solely possessing abstracts written in English, although there are also English-language and multi-language journals with articles not only in Armenian but also in other foreign languages. The aim of this article is to study the visibility of the (non-indexed) national Armenian journals in the WoS database through citation analysis. In consideration of the existence of a relevant Armenian “diaspora” in the world, this article also attempts to estimate its impact in terms of citation statistics. Design/methodology/approach For this end, we have identified citations to the national/domestic Armenian journals in the WoS database in comparison with the share of citations received from “diaspora” researchers (researchers of Armenian origin born in foreign countries and those originally from Armenia who have emigrated to foreign countries). Findings Among the 116 Armenian domestic journals analyzed (not indexed by WoS), only 47 were found to be cited in WoS. Of these journals, almost 12% are citations by “diaspora” researchers, most of which concern Social Science and Humanities journals. Research limitations Although the surnames of Armenians end with -i(y)an, sometimes, the Diaspora Armenians, surnames are changed or modified or they are not ending with -i(y)an, in this case we may fail to identify them. Practical implications This study can help to build new, more deep and comprehensive relations with scientific diasporas. Originality/value This study offers a new understanding of multifaced research collaboration with scientific diasporas and their role in internationalization of domestic journals.","PeriodicalId":92237,"journal":{"name":"Journal of data and information science (Warsaw, Poland)","volume":"8 1","pages":"93 - 117"},"PeriodicalIF":0.0,"publicationDate":"2023-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44902267","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract Purpose The aim of this study is to analyze the evolution of international research collaboration from 1980 to 2021. The study examines the main global patterns as well as those specific to individual countries, country groups, and different areas of research. Design/methodology/approach The study is based on the Web of Science Core collection database. More than 50 million publications are analyzed using co-authorship data. International collaboration is defined as publications having authors affiliated with institutions located in more than one country. Findings At the global level, the share of publications representing international collaboration has gradually increased from 4.7% in 1980 to 25.7% in 2021. The proportion of such publications within each country is higher and, in 2021, varied from less than 30% to more than 90%. There are notable disparities in the temporal trends, indicating that the process of internationalization has impacted countries in different ways. Several factors such as country size, income level, and geopolitics may explain the variance. Research limitations Not all international research collaboration results in joint co-authored scientific publications. International co-authorship is a partial indicator of such collaboration. Another limitation is that the applied full counting method does not take into account the number of authors representing in each country in the publication. Practical implications The study provides global averages, indicators, and concepts that can provide a useful framework of reference for further comparative studies of international research collaboration. Originality/value Long-term macro-level studies of international collaboration are rare, and as a novelty, this study includes an analysis by the World Bank’s division of countries into four income groups.
摘要目的本研究旨在分析1980-2021年国际研究合作的演变。该研究考察了主要的全球模式以及个别国家、国家集团和不同研究领域的具体模式。设计/方法论/方法本研究基于Web of Science核心收集数据库。使用合著者数据对5000多万份出版物进行了分析。国际合作是指作者隶属于一个以上国家机构的出版物。调查结果在全球范围内，代表国际合作的出版物所占比例从1980年的4.7%逐渐增加到2021年的25.7%。这些出版物在每个国家的比例都更高，2021年的比例从不到30%到超过90%不等。时间趋势存在显著差异，表明国际化进程对各国产生了不同的影响。国家规模、收入水平和地缘政治等几个因素可能解释了这种差异。研究局限性并非所有的国际研究合作都会导致联合撰写的科学出版物。国际合作是这种合作的部分指标。另一个限制是，所采用的全面计数方法没有考虑到出版物中每个国家的作者人数。实际意义该研究提供了全球平均值、指标和概念，可以为国际研究合作的进一步比较研究提供有用的参考框架。原创性/价值国际合作的长期宏观层面研究很少，作为一项新颖的研究，这项研究包括世界银行将国家划分为四个收入群体的分析。
{"title":"Global trends in international research collaboration, 1980-2021","authors":"D. Aksnes, G. Sivertsen","doi":"10.2478/jdis-2023-0015","DOIUrl":"https://doi.org/10.2478/jdis-2023-0015","url":null,"abstract":"Abstract Purpose The aim of this study is to analyze the evolution of international research collaboration from 1980 to 2021. The study examines the main global patterns as well as those specific to individual countries, country groups, and different areas of research. Design/methodology/approach The study is based on the Web of Science Core collection database. More than 50 million publications are analyzed using co-authorship data. International collaboration is defined as publications having authors affiliated with institutions located in more than one country. Findings At the global level, the share of publications representing international collaboration has gradually increased from 4.7% in 1980 to 25.7% in 2021. The proportion of such publications within each country is higher and, in 2021, varied from less than 30% to more than 90%. There are notable disparities in the temporal trends, indicating that the process of internationalization has impacted countries in different ways. Several factors such as country size, income level, and geopolitics may explain the variance. Research limitations Not all international research collaboration results in joint co-authored scientific publications. International co-authorship is a partial indicator of such collaboration. Another limitation is that the applied full counting method does not take into account the number of authors representing in each country in the publication. Practical implications The study provides global averages, indicators, and concepts that can provide a useful framework of reference for further comparative studies of international research collaboration. Originality/value Long-term macro-level studies of international collaboration are rare, and as a novelty, this study includes an analysis by the World Bank’s division of countries into four income groups.","PeriodicalId":92237,"journal":{"name":"Journal of data and information science (Warsaw, Poland)","volume":"8 1","pages":"26 - 42"},"PeriodicalIF":0.0,"publicationDate":"2023-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43577441","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract Purpose Diaspora researchers work in one country but have ancestral origins in another, either through moves during a research career (mobile diaspora researchers) or by starting research in the target country (embedded diaspora researchers). Whilst mobile researchers might be tracked through affiliation changes in bibliometric databases, embedded researchers cannot. This article reports an evidence-based discussion of which countries’ diaspora researchers can be partially tracked using first or last names, addressing this limitation. Design/methodology/approach A frequency analysis of first and last names of authors of all Scopus journal articles 2001-2021 for 200 countries or regions. Findings There are great variations in the extent to which first or last names are uniquely national, from Monserrat (no unique first names) to Thailand (81% unique last names). Nevertheless, most countries have a subset of first or last names that are relatively unique. For the 50 countries with the most researchers, authors with relatively national names are always more likely to research their name-associated country, suggesting a continued national association. Lists of researchers’ first and last name frequencies and proportions are provided for 200 countries/regions. Research limitations Only one period is tracked (2001-2021) and no attempt was made to validate the ancestral origins of any researcher. Practical implications Simple name heuristics can be used to identify the international spread of a sample of most countries’ diaspora researchers, but some manual checks of individual names are needed to weed out false matches. This can supplement mobile researcher data from bibliometric databases. Originality/value This is the first attempt to list name associations for the authors of all countries and large regions, and to identify the countries for which diaspora researchers could be tracked by name.
{"title":"Can first or last name uniqueness help to identify diaspora researchers from any country?","authors":"M. Thelwall","doi":"10.2478/jdis-2023-0013","DOIUrl":"https://doi.org/10.2478/jdis-2023-0013","url":null,"abstract":"Abstract Purpose Diaspora researchers work in one country but have ancestral origins in another, either through moves during a research career (mobile diaspora researchers) or by starting research in the target country (embedded diaspora researchers). Whilst mobile researchers might be tracked through affiliation changes in bibliometric databases, embedded researchers cannot. This article reports an evidence-based discussion of which countries’ diaspora researchers can be partially tracked using first or last names, addressing this limitation. Design/methodology/approach A frequency analysis of first and last names of authors of all Scopus journal articles 2001-2021 for 200 countries or regions. Findings There are great variations in the extent to which first or last names are uniquely national, from Monserrat (no unique first names) to Thailand (81% unique last names). Nevertheless, most countries have a subset of first or last names that are relatively unique. For the 50 countries with the most researchers, authors with relatively national names are always more likely to research their name-associated country, suggesting a continued national association. Lists of researchers’ first and last name frequencies and proportions are provided for 200 countries/regions. Research limitations Only one period is tracked (2001-2021) and no attempt was made to validate the ancestral origins of any researcher. Practical implications Simple name heuristics can be used to identify the international spread of a sample of most countries’ diaspora researchers, but some manual checks of individual names are needed to weed out false matches. This can supplement mobile researcher data from bibliometric databases. Originality/value This is the first attempt to list name associations for the authors of all countries and large regions, and to identify the countries for which diaspora researchers could be tracked by name.","PeriodicalId":92237,"journal":{"name":"Journal of data and information science (Warsaw, Poland)","volume":"8 1","pages":"1 - 25"},"PeriodicalIF":0.0,"publicationDate":"2023-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41900076","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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