Abstract A naïve Bayes approach to theory confirmation is used to compute the posterior probabilities for a series of four models of DNA considered by James Watson and Francis Crick in the early 1950s using multiple forms of evidence considered relevant at the time. Conditional probabilities for the evidence given each model are estimated from historical sources and manually assigned using a scale of five probabilities ranging from strongly consistent to strongly inconsistent. Alternative or competing theories are defined for each model based on preceding models in the series. Prior probabilities are also set based on the posterior probabilities of these earlier models. A dramatic increase in posterior probability is seen for the final double helix model compared to earlier models in the series, which is interpreted as a form of “Bayesian surprise” leading to the sense that a “discovery” was made. Implications for theory choice in the history of science are discussed.
{"title":"Bayesian history of science: The case of Watson and Crick and the structure of DNA","authors":"H. Small","doi":"10.1162/qss_a_00233","DOIUrl":"https://doi.org/10.1162/qss_a_00233","url":null,"abstract":"Abstract A naïve Bayes approach to theory confirmation is used to compute the posterior probabilities for a series of four models of DNA considered by James Watson and Francis Crick in the early 1950s using multiple forms of evidence considered relevant at the time. Conditional probabilities for the evidence given each model are estimated from historical sources and manually assigned using a scale of five probabilities ranging from strongly consistent to strongly inconsistent. Alternative or competing theories are defined for each model based on preceding models in the series. Prior probabilities are also set based on the posterior probabilities of these earlier models. A dramatic increase in posterior probability is seen for the final double helix model compared to earlier models in the series, which is interpreted as a form of “Bayesian surprise” leading to the sense that a “discovery” was made. Implications for theory choice in the history of science are discussed.","PeriodicalId":34021,"journal":{"name":"Quantitative Science Studies","volume":"4 1","pages":"209-228"},"PeriodicalIF":6.4,"publicationDate":"2023-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48860977","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}
M. Thelwall, K. Kousha, Mahshid Abdoli, E. Stuart, Meiko Makita, Cristina I. Font Julián, Paul Wilson, Jonathan M. Levitt
Abstract Although funding is essential for some types of research and beneficial for others, it may constrain academic choice and creativity. Thus, it is important to check whether it ever seems unnecessary. Here we investigate whether funded U.K. research tends to be higher quality in all fields and for all major research funders. Based on peer review quality scores for 113,877 articles from all fields in the U.K.’s Research Excellence Framework (REF) 2021, we estimate that there are substantial disciplinary differences in the proportion of funded journal articles, from Theology and Religious Studies (16%+) to Biological Sciences (91%+). The results suggest that funded research is likely to be of higher quality overall, for all the largest research funders, and for 30 out of 34 REF Units of Assessment (disciplines or sets of disciplines), even after factoring out research team size. There are differences between funders in the average quality of the research supported, however. Funding seems particularly associated with higher research quality in health-related fields. The results do not show cause and effect and do not take into account the amount of funding received but are consistent with funding either improving research quality or being won by high-quality researchers or projects.
{"title":"Is research funding always beneficial? A cross-disciplinary analysis of U.K. research 2014–20","authors":"M. Thelwall, K. Kousha, Mahshid Abdoli, E. Stuart, Meiko Makita, Cristina I. Font Julián, Paul Wilson, Jonathan M. Levitt","doi":"10.1162/qss_a_00254","DOIUrl":"https://doi.org/10.1162/qss_a_00254","url":null,"abstract":"Abstract Although funding is essential for some types of research and beneficial for others, it may constrain academic choice and creativity. Thus, it is important to check whether it ever seems unnecessary. Here we investigate whether funded U.K. research tends to be higher quality in all fields and for all major research funders. Based on peer review quality scores for 113,877 articles from all fields in the U.K.’s Research Excellence Framework (REF) 2021, we estimate that there are substantial disciplinary differences in the proportion of funded journal articles, from Theology and Religious Studies (16%+) to Biological Sciences (91%+). The results suggest that funded research is likely to be of higher quality overall, for all the largest research funders, and for 30 out of 34 REF Units of Assessment (disciplines or sets of disciplines), even after factoring out research team size. There are differences between funders in the average quality of the research supported, however. Funding seems particularly associated with higher research quality in health-related fields. The results do not show cause and effect and do not take into account the amount of funding received but are consistent with funding either improving research quality or being won by high-quality researchers or projects.","PeriodicalId":34021,"journal":{"name":"Quantitative Science Studies","volume":"4 1","pages":"501-534"},"PeriodicalIF":6.4,"publicationDate":"2022-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42361428","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}
M. Thelwall, K. Kousha, Mahshid Abdoli, E. Stuart, Meiko Makita, Paul Wilson, Jonathan M. Levitt, Petr Knoth, M. Cancellieri
Abstract National research evaluation initiatives and incentive schemes choose between simplistic quantitative indicators and time-consuming peer/expert review, sometimes supported by bibliometrics. Here we assess whether machine learning could provide a third alternative, estimating article quality using more multiple bibliometric and metadata inputs. We investigated this using provisional three-level REF2021 peer review scores for 84,966 articles submitted to the U.K. Research Excellence Framework 2021, matching a Scopus record 2014–18 and with a substantial abstract. We found that accuracy is highest in the medical and physical sciences Units of Assessment (UoAs) and economics, reaching 42% above the baseline (72% overall) in the best case. This is based on 1,000 bibliometric inputs and half of the articles used for training in each UoA. Prediction accuracies above the baseline for the social science, mathematics, engineering, arts, and humanities UoAs were much lower or close to zero. The Random Forest Classifier (standard or ordinal) and Extreme Gradient Boosting Classifier algorithms performed best from the 32 tested. Accuracy was lower if UoAs were merged or replaced by Scopus broad categories. We increased accuracy with an active learning strategy and by selecting articles with higher prediction probabilities, but this substantially reduced the number of scores predicted.
{"title":"Predicting article quality scores with machine learning: The U.K. Research Excellence Framework","authors":"M. Thelwall, K. Kousha, Mahshid Abdoli, E. Stuart, Meiko Makita, Paul Wilson, Jonathan M. Levitt, Petr Knoth, M. Cancellieri","doi":"10.1162/qss_a_00258","DOIUrl":"https://doi.org/10.1162/qss_a_00258","url":null,"abstract":"Abstract National research evaluation initiatives and incentive schemes choose between simplistic quantitative indicators and time-consuming peer/expert review, sometimes supported by bibliometrics. Here we assess whether machine learning could provide a third alternative, estimating article quality using more multiple bibliometric and metadata inputs. We investigated this using provisional three-level REF2021 peer review scores for 84,966 articles submitted to the U.K. Research Excellence Framework 2021, matching a Scopus record 2014–18 and with a substantial abstract. We found that accuracy is highest in the medical and physical sciences Units of Assessment (UoAs) and economics, reaching 42% above the baseline (72% overall) in the best case. This is based on 1,000 bibliometric inputs and half of the articles used for training in each UoA. Prediction accuracies above the baseline for the social science, mathematics, engineering, arts, and humanities UoAs were much lower or close to zero. The Random Forest Classifier (standard or ordinal) and Extreme Gradient Boosting Classifier algorithms performed best from the 32 tested. Accuracy was lower if UoAs were merged or replaced by Scopus broad categories. We increased accuracy with an active learning strategy and by selecting articles with higher prediction probabilities, but this substantially reduced the number of scores predicted.","PeriodicalId":34021,"journal":{"name":"Quantitative Science Studies","volume":"4 1","pages":"547-573"},"PeriodicalIF":6.4,"publicationDate":"2022-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41833406","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}
S. Khanna, Jon Ball, Juan Pablo Alperin, J. Willinsky
Abstract By analyzing 25,671 journals largely absent from common journal counts, as well as Web of Science and Scopus, this study demonstrates that scholarly communication is more of a global endeavor than is commonly credited. These journals, employing the open-source publishing platform Open Journal Systems (OJS), have published 5.8 million items; they are in 136 countries, with 79.9% in the Global South and 84.2% following the OA diamond model (charging neither reader nor author). A substantial proportion of journals operate in more than one language (48.3%), with research published in 60 languages (led by English, Indonesian, Spanish, and Portuguese). The journals are distributed across the social sciences (45.9%), STEM (40.3%), and the humanities (13.8%). For all their geographic, linguistic, and disciplinary diversity, 1.2% are indexed in the Web of Science and 5.7% in Scopus. On the other hand, 1.0% are found in Cabell’s Predatory Reports, and 1.4% show up in Beall’s (2021) questionable list. This paper seeks to both contribute to and historically situate the expanded scale and diversity of scholarly publishing in the hope that this recognition may assist humankind in taking full advantage of what is increasingly a global research enterprise.
{"title":"Recalibrating the scope of scholarly publishing: A modest step in a vast decolonization process","authors":"S. Khanna, Jon Ball, Juan Pablo Alperin, J. Willinsky","doi":"10.1162/qss_a_00228","DOIUrl":"https://doi.org/10.1162/qss_a_00228","url":null,"abstract":"Abstract By analyzing 25,671 journals largely absent from common journal counts, as well as Web of Science and Scopus, this study demonstrates that scholarly communication is more of a global endeavor than is commonly credited. These journals, employing the open-source publishing platform Open Journal Systems (OJS), have published 5.8 million items; they are in 136 countries, with 79.9% in the Global South and 84.2% following the OA diamond model (charging neither reader nor author). A substantial proportion of journals operate in more than one language (48.3%), with research published in 60 languages (led by English, Indonesian, Spanish, and Portuguese). The journals are distributed across the social sciences (45.9%), STEM (40.3%), and the humanities (13.8%). For all their geographic, linguistic, and disciplinary diversity, 1.2% are indexed in the Web of Science and 5.7% in Scopus. On the other hand, 1.0% are found in Cabell’s Predatory Reports, and 1.4% show up in Beall’s (2021) questionable list. This paper seeks to both contribute to and historically situate the expanded scale and diversity of scholarly publishing in the hope that this recognition may assist humankind in taking full advantage of what is increasingly a global research enterprise.","PeriodicalId":34021,"journal":{"name":"Quantitative Science Studies","volume":"3 1","pages":"912-930"},"PeriodicalIF":6.4,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43408000","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. Sachini, Konstantinos Sioumalas-Christodoulou, S. Christopoulos, Nikolaos Karampekios
Abstract Subject area classification is an important first phase in the entire process involved in bibliometrics. In this paper, we explore the possibility of using automated algorithms for classifying scientific papers related to Artificial Intelligence at the document level. The current process is semimanual and journal based, a realization that, we argue, opens up the potential for inaccuracies. To counter this, our proposed automated approach makes use of neural networks, specifically BERT. The classification accuracy of our model reaches 96.5%. In addition, the model was used for further classifying documents from 26 different subject areas from the Scopus database. Our findings indicate that a significant subset of existing Computer Science, Decision Science, and Mathematics publications could potentially be classified as AI-related. The same holds in particular cases in other science fields such as Medicine and Psychology or Arts and Humanities. The above indicate that in subject area classification processes, there is room for automatic approaches to be utilized in a complementary manner with traditional manual procedures.
{"title":"AI for AI: Using AI methods for classifying AI science documents","authors":"E. Sachini, Konstantinos Sioumalas-Christodoulou, S. Christopoulos, Nikolaos Karampekios","doi":"10.1162/qss_a_00223","DOIUrl":"https://doi.org/10.1162/qss_a_00223","url":null,"abstract":"Abstract Subject area classification is an important first phase in the entire process involved in bibliometrics. In this paper, we explore the possibility of using automated algorithms for classifying scientific papers related to Artificial Intelligence at the document level. The current process is semimanual and journal based, a realization that, we argue, opens up the potential for inaccuracies. To counter this, our proposed automated approach makes use of neural networks, specifically BERT. The classification accuracy of our model reaches 96.5%. In addition, the model was used for further classifying documents from 26 different subject areas from the Scopus database. Our findings indicate that a significant subset of existing Computer Science, Decision Science, and Mathematics publications could potentially be classified as AI-related. The same holds in particular cases in other science fields such as Medicine and Psychology or Arts and Humanities. The above indicate that in subject area classification processes, there is room for automatic approaches to be utilized in a complementary manner with traditional manual procedures.","PeriodicalId":34021,"journal":{"name":"Quantitative Science Studies","volume":"3 1","pages":"1119-1132"},"PeriodicalIF":6.4,"publicationDate":"2022-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43148497","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}
Nancy Pontika, Thomas Klebel, Antonia Correia, Hannah Metzler, Petr Knoth, T. Ross-Hellauer
Abstract The need to reform research assessment processes related to career advancement at research institutions has become increasingly recognized in recent years, especially to better foster open and responsible research practices. Current assessment criteria are believed to focus too heavily on inappropriate criteria related to productivity and quantity as opposed to quality, collaborative open research practices, and the socioeconomic impact of research. Evidence of the extent of these issues is urgently needed to inform actions for reform, however. We analyze current practices as revealed by documentation on institutional review, promotion, and tenure (RPT) processes in seven countries (Austria, Brazil, Germany, India, Portugal, the United Kingdom and the United States). Through systematic coding and analysis of 143 RPT policy documents from 107 institutions for the prevalence of 17 criteria (including those related to qualitative or quantitative assessment of research, service to the institution or profession, and open and responsible research practices), we compare assessment practices across a range of international institutions to significantly broaden this evidence base. Although the prevalence of indicators varies considerably between countries, overall we find that currently open and responsible research practices are minimally rewarded and problematic practices of quantification continue to dominate.
{"title":"Indicators of research quality, quantity, openness, and responsibility in institutional review, promotion, and tenure policies across seven countries","authors":"Nancy Pontika, Thomas Klebel, Antonia Correia, Hannah Metzler, Petr Knoth, T. Ross-Hellauer","doi":"10.1162/qss_a_00224","DOIUrl":"https://doi.org/10.1162/qss_a_00224","url":null,"abstract":"Abstract The need to reform research assessment processes related to career advancement at research institutions has become increasingly recognized in recent years, especially to better foster open and responsible research practices. Current assessment criteria are believed to focus too heavily on inappropriate criteria related to productivity and quantity as opposed to quality, collaborative open research practices, and the socioeconomic impact of research. Evidence of the extent of these issues is urgently needed to inform actions for reform, however. We analyze current practices as revealed by documentation on institutional review, promotion, and tenure (RPT) processes in seven countries (Austria, Brazil, Germany, India, Portugal, the United Kingdom and the United States). Through systematic coding and analysis of 143 RPT policy documents from 107 institutions for the prevalence of 17 criteria (including those related to qualitative or quantitative assessment of research, service to the institution or profession, and open and responsible research practices), we compare assessment practices across a range of international institutions to significantly broaden this evidence base. Although the prevalence of indicators varies considerably between countries, overall we find that currently open and responsible research practices are minimally rewarded and problematic practices of quantification continue to dominate.","PeriodicalId":34021,"journal":{"name":"Quantitative Science Studies","volume":"3 1","pages":"888-911"},"PeriodicalIF":6.4,"publicationDate":"2022-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46252894","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 Informal knowledge constantly transitions into formal domain knowledge in the dynamic knowledge base. This article focuses on an integrative understanding of the knowledge role transition from the perspective of knowledge codification. The transition process is characterized by several dynamics involving a variety of bibliometric entities, such as authors, keywords, institutions, and venues. We thereby designed a series of temporal and cumulative indicators to respectively explore transition possibility (whether new knowledge could be transitioned into formal knowledge) and transition pace (how long it would take). By analyzing the large-scale metadata of publications that contain informal knowledge and formal knowledge in the PubMed database, we find that multidimensional variables are essential to comprehensively understand knowledge role transition. More significantly, early funding support is more important for improving transition pace; journal impact has a positive correlation with the transition possibility but a negative correlation with transition pace; and weaker knowledge relatedness raises the transition possibility, whereas stronger knowledge relatedness improves the transition pace.
{"title":"Understanding knowledge role transitions: A perspective of knowledge codification","authors":"Jinqing Yang, Wei Lu, Yong Huang, Qikai Cheng, Li Zhang, Shengzhi Huang","doi":"10.1162/qss_a_00221","DOIUrl":"https://doi.org/10.1162/qss_a_00221","url":null,"abstract":"Abstract Informal knowledge constantly transitions into formal domain knowledge in the dynamic knowledge base. This article focuses on an integrative understanding of the knowledge role transition from the perspective of knowledge codification. The transition process is characterized by several dynamics involving a variety of bibliometric entities, such as authors, keywords, institutions, and venues. We thereby designed a series of temporal and cumulative indicators to respectively explore transition possibility (whether new knowledge could be transitioned into formal knowledge) and transition pace (how long it would take). By analyzing the large-scale metadata of publications that contain informal knowledge and formal knowledge in the PubMed database, we find that multidimensional variables are essential to comprehensively understand knowledge role transition. More significantly, early funding support is more important for improving transition pace; journal impact has a positive correlation with the transition possibility but a negative correlation with transition pace; and weaker knowledge relatedness raises the transition possibility, whereas stronger knowledge relatedness improves the transition pace.","PeriodicalId":34021,"journal":{"name":"Quantitative Science Studies","volume":"3 1","pages":"1133-1155"},"PeriodicalIF":6.4,"publicationDate":"2022-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45760339","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 Often, bibliometric mapping studies remain at a very abstract level when assessing the validity or accuracy of the generated maps. In this case study of citation-based mappings of a research specialty, we dig deeper into the topical structures generated by the chosen mapping approaches and examine their correspondence to a sociologically informed understanding of the research specialty in question. Starting from a lexically delineated bibliometric field data set, we create an internal map of invasion biology by clustering the direct citation network with the Leiden algorithm. We obtain a topic structure that seems largely ordered by the empirical objects studied (species and habitat). To complement this view, we generate an external map of invasion biology by projecting the field data set onto the global Centre for Science and Technology Studies (CWTS) field classification. To better understand the representation of invasion biology by this global map, we use a manually coded set of invasion biological publications and investigate their citation-based interlinking with the fields defined by the global field classification. Our analysis highlights the variety of types of topical relatedness and epistemic interdependency that citations can stand for. Unless we assume that invasion biology is unique in this regard, our analysis suggests that global algorithmic field classification approaches that use citation links indiscriminately may struggle to reconstruct research specialties.
{"title":"How to interpret algorithmically constructed topical structures of scientific fields? A case study of citation-based mappings of the research specialty of invasion biology","authors":"Matthias Held, T. Velden","doi":"10.1162/qss_a_00194","DOIUrl":"https://doi.org/10.1162/qss_a_00194","url":null,"abstract":"Abstract Often, bibliometric mapping studies remain at a very abstract level when assessing the validity or accuracy of the generated maps. In this case study of citation-based mappings of a research specialty, we dig deeper into the topical structures generated by the chosen mapping approaches and examine their correspondence to a sociologically informed understanding of the research specialty in question. Starting from a lexically delineated bibliometric field data set, we create an internal map of invasion biology by clustering the direct citation network with the Leiden algorithm. We obtain a topic structure that seems largely ordered by the empirical objects studied (species and habitat). To complement this view, we generate an external map of invasion biology by projecting the field data set onto the global Centre for Science and Technology Studies (CWTS) field classification. To better understand the representation of invasion biology by this global map, we use a manually coded set of invasion biological publications and investigate their citation-based interlinking with the fields defined by the global field classification. Our analysis highlights the variety of types of topical relatedness and epistemic interdependency that citations can stand for. Unless we assume that invasion biology is unique in this regard, our analysis suggests that global algorithmic field classification approaches that use citation links indiscriminately may struggle to reconstruct research specialties.","PeriodicalId":34021,"journal":{"name":"Quantitative Science Studies","volume":"3 1","pages":"651-671"},"PeriodicalIF":6.4,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48403865","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}
This issue of Quantitative Science Studies features the article “Predatory publishing in Scopus: Evidence on cross-country differences,” coauthored by Vít Macháček and Martin Srholec. Based on the Scopus database, this article studies how likely different countries are to publish in so-called predatory journals. Journals suspected to be predatory are identified using the well-known (and controversial) list of potentially predatory publishers and journals compiled by former librarian Jeffrey Beall.
{"title":"The challenges of scientometric studies of predatory publishing","authors":"L. Waltman, V. Larivière","doi":"10.1162/qss_e_00214","DOIUrl":"https://doi.org/10.1162/qss_e_00214","url":null,"abstract":"This issue of Quantitative Science Studies features the article “Predatory publishing in Scopus: Evidence on cross-country differences,” coauthored by Vít Macháček and Martin Srholec. Based on the Scopus database, this article studies how likely different countries are to publish in so-called predatory journals. Journals suspected to be predatory are identified using the well-known (and controversial) list of potentially predatory publishers and journals compiled by former librarian Jeffrey Beall.","PeriodicalId":34021,"journal":{"name":"Quantitative Science Studies","volume":"3 1","pages":"857-858"},"PeriodicalIF":6.4,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48745981","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 The current value of link counts as supplementary measures of the formal quality and impact of journals is analyzed, considering an open access megapublisher (MDPI) as a case study. We analyzed 352 journals through 21 citation-based and link-based journal-level indicators, using Scopus (523,935 publications) and Majestic (567,900 links) as data sources. Given the statistically significant strong positive Spearman correlations achieved, it is concluded that link-based indicators mainly reflect the quality (indexed in Scopus), size (publication output), and impact (citations received) of MDPI’s journals. In addition, link data are significantly greater for those MDPI journals covering many subjects (generalist journals). However, nonstatistically significant differences are found between subject categories, which can be partially attributed to the “series title profile” effect of MDPI. Further research is necessary to test whether link-based indicators can be used as informative measures of journals’ current research impact beyond the specific characteristics of MDPI.
{"title":"Are link-based and citation-based journal metrics correlated? An Open Access megapublisher case study","authors":"E. Orduña-Malea, Isidro F. Aguillo","doi":"10.1162/qss_a_00199","DOIUrl":"https://doi.org/10.1162/qss_a_00199","url":null,"abstract":"Abstract The current value of link counts as supplementary measures of the formal quality and impact of journals is analyzed, considering an open access megapublisher (MDPI) as a case study. We analyzed 352 journals through 21 citation-based and link-based journal-level indicators, using Scopus (523,935 publications) and Majestic (567,900 links) as data sources. Given the statistically significant strong positive Spearman correlations achieved, it is concluded that link-based indicators mainly reflect the quality (indexed in Scopus), size (publication output), and impact (citations received) of MDPI’s journals. In addition, link data are significantly greater for those MDPI journals covering many subjects (generalist journals). However, nonstatistically significant differences are found between subject categories, which can be partially attributed to the “series title profile” effect of MDPI. Further research is necessary to test whether link-based indicators can be used as informative measures of journals’ current research impact beyond the specific characteristics of MDPI.","PeriodicalId":34021,"journal":{"name":"Quantitative Science Studies","volume":"3 1","pages":"793-814"},"PeriodicalIF":6.4,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42597381","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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