Bibliometrics Is Valuable Science. Why Do Some Journals Seem to Oppose It?

<abstract xmlns="http://www.w3.org/1999/xhtml">

<sec><h3>Purpose</h3><p>A new point of view in the study of impact is introduced.</p></sec>
<sec><h3>Design/methodology/approach</h3><p>Using fundamental theorems in real analysis we study the convergence of well-known impact measures.</p></sec>
<sec><h3>Findings</h3><p>We show that pointwise convergence is maintained by all well-known impact bundles (such as the h-, g-, and R-bundle) and that the μ-bundle even maintains uniform convergence. Based on these results, a classification of impact bundles is given.</p></sec>
<sec><h3>Research limitations</h3><p>As for all impact studies, it is just impossible to study all measures in depth.</p></sec>
<sec><h3>Practical implications</h3><p>It is proposed to include convergence properties in the study of impact measures.</p></sec>
<sec><h3>Originality/value</h3><p>This article is the first to present a bundle classification based on convergence properties of impact bundles.</p></sec>
</abstract>

Convergence of Impact Measures and Impact Bundles

<abstract xmlns="http://www.w3.org/1999/xhtml">

<sec><h3>Purpose</h3>
<p>Measuring the exact technology complementarity between different institutions is necessary to obtain complementary technology resources for R&amp;D cooperation.</p>
</sec>
<sec><h3>Design/methodology/approach</h3>
<p>This study constructs a morphology-driven method for measuring technology complementarity, taking medical field as an example. First, we calculate semantic similarities between subjects (S and S) and action-objects (AO and AO) based on the Metathesaurus, forming clusters of S and AO based on a semantic similarity matrix. Second, we identify key technology issues and methods based on clusters of S and AO. Third, a technology morphology matrix of several dimensions is constructed using morphology analysis, and the matrix is filled with subjects -action-objects (SAO) structures according to corresponding key technology issues and methods for different institutions. Finally, the technology morphology matrix is used to measure the technology complementarity between different institutions based on SAO.</p>
</sec>
<sec><h3>Findings</h3>
<p>The improved technology complementarity method based on SAO is more of a supplementary and refined framework for the traditional IPC method.</p>
</sec>
<sec><h3>Research limitations</h3>
<p>In future studies we will reprocess and identify the SAO structures which were not in the technology morphology matrix, and find other methods to characterize key technical issues and methods. Furthermore, we will add the comparison between proposed method and traditional and mostly used complementarity measurement method based on industry chain and industry code.</p>
</sec>
<sec><h3>Practical implications</h3>
<p>This study takes medical field as an example. The morphology-driven method for measuring technology complementarity can be migrated and applied for any given field.</p>
</sec>
<sec><h3>Originality/value</h3>
<p>From the perspective of complementary technology resources, this study develops and tests a more accurate morphology-driven method for technology complementarity measurement.</p>
</sec>
</abstract>

China University of Petroleum (East China)

A Morphology-Driven Method for Measuring Technology Complementarity: Empirical Study Involving Alzheimer's Disease

<abstract xmlns="http://www.w3.org/1999/xhtml">

<sec><h3>Purpose</h3>
<p>Patent classification is one of the areas in Intellectual Property Analytics (IPA), and a growing use case since the number of patent applications has been increasing worldwide. We propose using machine learning algorithms to classify Portuguese patents and evaluate the performance of transfer learning methodologies to solve this task.</p>
</sec>
<sec><h3>Design/methodology/approach</h3>
<p>We applied three different approaches in this paper. First, we used a dataset available by INPI to explore traditional machine learning algorithms and ensemble methods. After preprocessing data by applying TF-IDF, FastText and Doc2Vec, the models were evaluated by cross-validation in 5 folds. In a second approach, we used two different Neural Networks architectures, a Convolutional Neural Network (CNN) and a bi-directional Long Short-Term Memory (BiLSTM). Finally, we used pre-trained BERT, DistilBERT, and ULMFiT models in the third approach.</p>
</sec>
<sec><h3>Findings</h3>
<p>BERTTimbau, a BERT architecture model pre-trained on a large Portuguese corpus, presented the best results for the task, even though with a performance of only 4% superior to a LinearSVC model using TF-IDF feature engineering.</p>
</sec>
<sec><h3>Research limitations</h3>
<p>The dataset was highly imbalanced, as usual in patent applications, so the classes with the lowest samples were expected to present the worst performance. That result happened in some cases, especially in classes with less than 60 training samples.</p>
</sec>
<sec><h3>Practical implications</h3>
<p>Patent classification is challenging because of the hierarchical classification system, the context overlap, and the underrepresentation of the classes. However, the final model presented an acceptable performance given the size of the dataset and the task complexity. This model can support the decision and improve the time by proposing a category in the second level of ICP, which is one of the critical phases of the grant patent process.</p>
</sec>
<sec><h3>Originality/value</h3>
<p>To our knowledge, the proposed models were never implemented for Portuguese patent classification.</p>
</sec>
</abstract>

A Use Case of Patent Classification Using Deep Learning with Transfer Learning

<abstract xmlns="http://www.w3.org/1999/xhtml">

<sec><h3>Purpose</h3>
<p>This article explores the implications of publication requirements for the research output of Ukrainian academics in Scopus in 1999–2019. As such it contributes to the existing body of knowledge on quantitative and qualitative effects of research evaluation policies.</p>
</sec>
<sec><h3>Design/methodology/approach</h3>
<p>Three metrics were chosen to analyse the implications of publication requirements for the quality of research output: publications in predatory journals, publications in local journals and publications per SNIP quartile from the disciplinary perspective.</p>
</sec>
<sec><h3>Findings</h3>
<p>Study results highlight, that, firstly, publications of Ukrainian authors in predatory journals rose to 1% in 2019. Secondly, the share of publications in local journals reached the peak of 47.3% in 2015. In 2019 it fell to 31.8%. Thirdly, though the total number of publications has risen dramatically since 2011, but the share of Q3+Q4 has exceeded the share of Q1+Q2. To summarise, the study findings highligh, that research evaluation policies are required to contain not only quantitative but also qualitative criteria.</p>
</sec>
<sec><h3>Research limitation</h3>
<p>The study does not explore in detail the effects of a particular type of publication requirements.</p>
</sec>
<sec><h3>Practical implications</h3>
<p>The findings of the study have practical implications for policymakers and university managers aimed to develop research evaluation policies.</p>
</sec>
<sec><h3>Originality/value</h3>
<p>This paper gains insights into the effects of publication requirements on the research output of Ukrainian academics in Scopus.</p>
</sec>
</abstract>

Implications of Publication Requirements for the Research Output of Ukrainian Academics in Scopus in 1999–2019

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Journal of Data and Information Science

Journal of Data and Information Science (JDIS, formerly Chinese Journal of Library and Information Science), sponsored by the Chinese Academy of Sciences (CAS) and published quarterly by the National Science Library of CAS, is the first internationally published English-language academic journal in Library and Information Science and related fields from China.  The Journal of Data and Information Science (JDIS) focuses on data-based research oriented toward the exploration of scientific research and innovation. The main areas of interest are science of science, evidence-based policymaking, research evaluation, computational social science, and scientometrics/bibliometrics/altmetrics/ informetrics. Emphasis is given to research that focuses on data, analytics, and knowledge discovery, and supports decision making and science policy. This includes modeling, innovation, data security, media and communications, and social development. Topics may include studies of metadata or full content data, text or non-textural data, structured or non-structural data, domain-specific or cross-domain data, and dynamic or interactive data.  Specific topic areas may include (but are not limited to):    Knowledge organization  Knowledge discovery and data mining  Knowledge integration and fusion  Semantic Web  Science of science  Bibliometrics and scientometrics  Analytic and diagnostic informetrics  Competitive intelligence  Predictive analysis  Social network analysis and metrics  Semantic and interactively analytic retrieval  Evidence-based policy analysis  Intelligent knowledge production  Knowledge-driven workflow management and decision-making  Knowledge-driven collaboration and its management  Domain knowledge infrastructure with knowledge fusion and analytics  Training for data &amp; information scientists  Development of data and information services    JDIS publishes theoretical and empirical work. Systematic reviews are welcome and applied research in development of advanced methods, services, and best practices is also an important part. But simple application of established informetrics on a specific research field or country is out of the scope.Welcome to submit your papers to JDIS.  Why subscribe and read  JDIS is the first and only English journal from China in Library and Information Science and related fields. With an aim to disseminate the cutting-edge research in these fields, it is devoted to the study and application of the theories, methods, techniques, services, and infrastructural facilities using big data to support knowledge discovery for decision and policy making. The basic emphasis is big data-based, analytics centered, knowledge discovery driven, and decision making supporting. JDIS has gathered a big body of high profile experts across the world who contribute their research to the journal. The international authors account for around 62% in its first publication year (2016).  Why submit  JDIS is the first and only English journal from China in Library and Information Science and related fields. It owns a number of world front-line scholars as editorial board members or reviewers. The turnaround time on average for a manuscript from submission to final decision is less than two and a half months.  Archiving  Sciendo archives the contents of this journal in Portico- digital long-term preservation service of scholarly books, journals and collections.  Plagiarism Policy  The editorial board is participating in a growing community of Similarity Check System's users in order to ensure that the content published is original and trustworthy. Similarity Check is a medium that allows for comprehensive manuscripts screening, aimed to eliminate plagiarism and provide a high standard and quality peer-review process. 

Journal of Data and Information Science

Volume 7 (2022): Issue 3 (August 2022)

Opinion

Bibliometrics Is Valuable Science. Why Do Some Journals Seem to Oppose It?

Research Paper

Convergence of Impact Measures and Impact Bundles

A Morphology-Driven Method for Measuring Technology Complementarity: Empirical Study Involving Alzheimer's Disease

A Use Case of Patent Classification Using Deep Learning with Transfer Learning

Implications of Publication Requirements for the Research Output of Ukrainian Academics in Scopus in 1999–2019

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