<abstract><title style='display:none'>Abstract</title><sec id="j_jdis-2017-0006_s_006_w2aab2b8c32b1b7b1aab1c15b1Aa"><title style='display:none'>Purpose</title>We present a systematic review of the literature concerning major aspects of science mapping to serve two primary purposes: First, to demonstrate the use of a science mapping approach to perform the review so that researchers may apply the procedure to the review of a scientific domain of their own interest, and second, to identify major areas of research activities concerning science mapping, intellectual milestones in the development of key specialties, evolutionary stages of major specialties involved, and the dynamics of transitions from one specialty to another.</sec><sec id="j_jdis-2017-0006_s_007_w2aab2b8c32b1b7b1aab1c15b2Aa"><title style='display:none'>Design/methodology/approach</title>We first introduce a theoretical framework of the evolution of a scientific specialty. Then we demonstrate a generic search strategy that can be used to construct a representative dataset of bibliographic records of a domain of research. Next, progressively synthesized co-citation networks are constructed and visualized to aid visual analytic studies of the domain’s structural and dynamic patterns and trends. Finally, trajectories of citations made by particular types of authors and articles are presented to illustrate the predictive potential of the analytic approach.</sec><sec id="j_jdis-2017-0006_s_008_w2aab2b8c32b1b7b1aab1c15b3Aa"><title style='display:none'>Findings</title>The evolution of the science mapping research involves the development of a number of interrelated specialties. Four major specialties are discussed in detail in terms of four evolutionary stages: conceptualization, tool construction, application, and codification. Underlying connections between major specialties are also explored. The predictive analysis demonstrates citations trajectories of potentially transformative contributions.</sec><sec id="j_jdis-2017-0006_s_009_w2aab2b8c32b1b7b1aab1c15b4Aa"><title style='display:none'>Research limitations</title>The systematic review is primarily guided by citation patterns in the dataset retrieved from the literature. The scope of the data is limited by the source of the retrieval, i.e. the Web of Science, and the composite query used. An iterative query refinement is possible if one would like to improve the data quality, although the current approach serves our purpose adequately. More in-depth analyses of each specialty would be more revealing by incorporating additional methods such as citation context analysis and studies of other aspects of scholarly publications.</sec><sec id="j_jdis-2017-0006_s_010_w2aab2b8c32b1b7b1aab1c15b5Aa"><title style='display:none'>Practical implications</title>The underlying analytic process of science mapping serves many practical needs, notably bibliometric mapping, knowledge domain visualization, and visualization of scientific literature. In order to master such a complex process of science mapping, researchers often need to develop a diverse set of skills and knowledge that may span multiple disciplines. The approach demonstrated in this article provides a generic method for conducting a systematic review.</sec><sec id="j_jdis-2017-0006_s_011_w2aab2b8c32b1b7b1aab1c15b6Aa"><title style='display:none'>Originality/value</title>Incorporating the evolutionary stages of a specialty into the visual analytic study of a research domain is innovative. It provides a systematic methodology for researchers to achieve a good understanding of how scientific fields evolve, to recognize potentially insightful patterns from visually encoded signs, and to synthesize various information so as to capture the state of the art of the domain.</sec></abstract>

Science Mapping: A Systematic Review of the Literature

<abstract><title style='display:none'>Abstract</title><sec id="j_jdis-2019-0001_s_006_w2aab3b7b1b1b6b1aab1c17b1Aa"><title style='display:none' id="d4944091e3411_w2aab3b7b1b1b6b1aab1c17b1aAa">Purpose</title>The purpose of this study is to assess the coverage of the scientific literature in Scopus and Web of Science from the perspective of research evaluation.</sec><sec id="j_jdis-2019-0001_s_007_w2aab3b7b1b1b6b1aab1c17b2Aa"><title style='display:none' id="d4944091e3419_w2aab3b7b1b1b6b1aab1c17b2aAa">Design/methodology/approach</title>The academic communities of Norway have agreed on certain criteria for what should be included as original research publications in research evaluation and funding contexts. These criteria have been applied since 2004 in a comprehensive bibliographic database called the Norwegian Science Index (NSI). The relative coverages of Scopus and Web of Science are compared with regard to publication type, field of research and language.</sec><sec id="j_jdis-2019-0001_s_008_w2aab3b7b1b1b6b1aab1c17b3Aa"><title style='display:none' id="d4944091e3427_w2aab3b7b1b1b6b1aab1c17b3aAa">Findings</title>Our results show that Scopus covers 72 percent of the total Norwegian scientific and scholarly publication output in 2015 and 2016, while the corresponding figure for Web of Science Core Collection is 69 percent. The coverages are most comprehensive in medicine and health (89 and 87 percent) and in the natural sciences and technology (85 and 84 percent). The social sciences (48 percent in Scopus and 40 percent in Web of Science Core Collection) and particularly the humanities (27 and 23 percent) are much less covered in the two international data sources.</sec><sec id="j_jdis-2019-0001_s_009_w2aab3b7b1b1b6b1aab1c17b4Aa"><title style='display:none' id="d4944091e3435_w2aab3b7b1b1b6b1aab1c17b4aAa">Research limitation</title>Comparing with data from only one country is a limitation of the study, but the criteria used to define a country’s scientific output as well as the identification of patterns of field-dependent partial representations in Scopus and Web of Science should be recognizable and useful also for other countries.</sec><sec id="j_jdis-2019-0001_s_010_w2aab3b7b1b1b6b1aab1c17b5Aa"><title style='display:none' id="d4944091e3444_w2aab3b7b1b1b6b1aab1c17b5aAa">Originality/value</title>The novelty of this study is the criteria-based approach to studying coverage problems in the two data sources.</sec></abstract>

A Criteria-based Assessment of the Coverage of Scopus and Web of Science

<abstract><title style='display:none'>Abstract</title>Due to the recent explosion of big data, our society has been rapidly going through digital transformation and entering a new world with numerous eye-opening developments. These new trends impact the society and future jobs, and thus student careers. At the heart of this digital transformation is data science, the discipline that makes sense of big data. With many rapidly emerging digital challenges ahead of us, this article discusses perspectives on iSchools’ opportunities and suggestions in data science education. We argue that iSchools should empower their students with “information computing” disciplines, which we define as the ability to solve problems and create values, information, and knowledge using tools in application domains. As specific approaches to enforcing information computing disciplines in data science education, we suggest the three foci of user-based, tool-based, and application-based. These three foci will serve to differentiate the data science education of iSchools from that of computer science or business schools. We present a layered Data Science Education Framework (DSEF) with building blocks that include the three pillars of data science (people, technology, and data), computational thinking, data-driven paradigms, and data science lifecycles. Data science courses built on the top of this framework should thus be executed with user-based, tool-based, and application-based approaches. This framework will help our students think about data science problems from the big picture perspective and foster appropriate problem-solving skills in conjunction with broad perspectives of data science lifecycles. We hope the DSEF discussed in this article will help fellow iSchools in their design of new data science curricula.</abstract>

Big Data and Data Science: Opportunities and Challenges of iSchools

<abstract><title style='display:none'>Abstract</title><sec id="j_jdis-2017-0012_s_006_w2aab3b8c28b1b7b1aab1c15b1Aa"><title style='display:none'>Purpose</title>The purpose of the paper is to provide a framework for addressing the disconnect between metadata and data science. Data science cannot progress without metadata research. This paper takes steps toward advancing the synergy between metadata and data science, and identifies pathways for developing a more cohesive metadata research agenda in data science.</sec><sec id="j_jdis-2017-0012_s_007_w2aab3b8c28b1b7b1aab1c15b2Aa"><title style='display:none'>Design/methodology/approach</title>This paper identifies factors that challenge metadata research in the digital ecosystem, defines metadata and data science, and presents the concepts <italic>big metadata</italic>, <italic>smart metadata</italic>, and <italic>metadata capital</italic> as part of a metadata <italic>lingua franca</italic> connecting to data science.</sec><sec id="j_jdis-2017-0012_s_008_w2aab3b8c28b1b7b1aab1c15b3Aa"><title style='display:none'>Findings</title>The “utilitarian nature” and “historical and traditional views” of metadata are identified as two intersecting factors that have inhibited metadata research. Big metadata, smart metadata, and metadata capital are presented as part of a metadata <italic>lingua franca</italic> to help frame research in the data science research space.</sec><sec id="j_jdis-2017-0012_s_009_w2aab3b8c28b1b7b1aab1c15b4Aa"><title style='display:none'>Research limitations</title>There are additional, intersecting factors to consider that likely inhibit metadata research, and other significant metadata concepts to explore.</sec><sec id="j_jdis-2017-0012_s_010_w2aab3b8c28b1b7b1aab1c15b5Aa"><title style='display:none'>Practical implications</title>The immediate contribution of this work is that it may elicit response, critique, revision, or, more significantly, motivate research. The work presented can encourage more researchers to consider the significance of metadata as a research worthy topic within data science and the larger digital ecosystem.</sec><sec id="j_jdis-2017-0012_s_011_w2aab3b8c28b1b7b1aab1c15b6Aa"><title style='display:none'>Originality/value</title>Although metadata research has not kept pace with other data science topics, there is little attention directed to this problem. This is surprising, given that metadata is essential for data science endeavors. This examination synthesizes original and prior scholarship to provide new grounding for metadata research in data science.</sec></abstract>

Big Metadata, Smart Metadata, and Metadata Capital: Toward Greater Synergy Between Data Science and Metadata

<abstract><title style='display:none'>Abstract</title><sec id="j_jdis-2017-0002_s_007_w2aab2b8b7b1b7b1aab1c15b1Aa"><title style='display:none'>Purpose</title>First, to review the state-of-the-art in patent citation analysis, particularly characteristics of patent citations to scientific literature (scientific non-patent references, SNPRs). Second, to present a novel mapping approach to identify technology-relevant research based on the papers cited by and referring to the SNPRs.</sec><sec id="j_jdis-2017-0002_s_008_w2aab2b8b7b1b7b1aab1c15b2Aa"><title style='display:none'>Design/methodology/approach</title>In the review part we discuss the context of SNPRs such as the time lags between scientific achievements and inventions. Also patent-to-patent citation is addressed particularly because this type of patent citation analysis is a major element in the assessment of the economic value of patents. We also review the research on the role of universities and researchers in technological development, with important issues such as universities as sources of technological knowledge and inventor-author relations. We conclude the review part of this paper with an overview of recent research on mapping and network analysis of the science and technology interface and of technological progress in interaction with science. In the second part we apply new techniques for the direct visualization of the cited and citing relations of SNPRs, the mapping of the landscape around SNPRs by bibliographic coupling and co-citation analysis, and the mapping of the conceptual environment of SNPRs by keyword co-occurrence analysis.</sec><sec id="j_jdis-2017-0002_s_009_w2aab2b8b7b1b7b1aab1c15b3Aa"><title style='display:none'>Findings</title>We discuss several properties of SNPRs. Only a small minority of publications covered by the Web of Science or Scopus are cited by patents, about 3%–4%. However, for publications based on university-industry collaboration the number of SNPRs is considerably higher, around 15%. The proposed mapping methodology based on a “second order SNPR approach” enables a better assessment of the technological relevance of research.</sec><sec id="j_jdis-2017-0002_s_010_w2aab2b8b7b1b7b1aab1c15b4Aa"><title style='display:none'>Research limitations</title>The main limitation is that a more advanced merging of patent and publication data, in particular unification of author and inventor names, in still a necessity.</sec><sec id="j_jdis-2017-0002_s_011_w2aab2b8b7b1b7b1aab1c15b5Aa"><title style='display:none'>Practical implications</title>The proposed mapping methodology enables the creation of a database of technology-relevant papers (TRPs). In a bibliometric assessment the publications of research groups, research programs or institutes can be matched with the TRPs and thus the extent to which the work of groups, programs or institutes are relevant for technological development can be measured.</sec><sec id="j_jdis-2017-0002_s_012_w2aab2b8b7b1b7b1aab1c15b6Aa"><title style='display:none'>Originality/value</title>The review part examines a wide range of findings in the research of patent citation analysis. The mapping approach to identify a broad range of technology-relevant papers is novel and offers new opportunities in research evaluation practices.</sec></abstract>

Patent Citations Analysis and Its Value in Research Evaluation: A Review and a New Approach to Map Technology-relevant Research

<abstract>
<title style='display:none'>Abstract</title>
<sec><title style='display:none'>Purpose</title>
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.
</sec>
<sec><title style='display:none'>Design/methodology/approach</title>
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.
</sec>
<sec><title style='display:none'>Findings</title>
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.
</sec>
<sec><title style='display:none'>Research limitations</title>
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.
</sec>
<sec><title style='display:none'>Practical implications</title>
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.
</sec>
<sec><title style='display:none'>Originality/value</title>
To our knowledge, the proposed models were never implemented for Portuguese patent classification.
</sec>
</abstract>

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

<abstract>
<title style='display:none'>Abstract</title>
<sec>
<title style='display:none'>Purpose</title>
Assess whether ChatGPT 4.0 is accurate enough to perform research evaluations on journal articles to automate this time-consuming task.
</sec>
<sec>
<title style='display:none'>Design/methodology/approach</title>
Test the extent to which ChatGPT-4 can assess the quality of journal articles using a case study of the published scoring guidelines of the UK Research Excellence Framework (REF) 2021 to create a research evaluation ChatGPT. This was applied to 51 of my own articles and compared against my own quality judgements.
</sec>
<sec>
<title style='display:none'>Findings</title>
ChatGPT-4 can produce plausible document summaries and quality evaluation rationales that match the REF criteria. Its overall scores have weak correlations with my self-evaluation scores of the same documents (averaging r=0.281 over 15 iterations, with 8 being statistically significantly different from 0). In contrast, the average scores from the 15 iterations produced a statistically significant positive correlation of 0.509. Thus, averaging scores from multiple ChatGPT-4 rounds seems more effective than individual scores. The positive correlation may be due to ChatGPT being able to extract the author’s significance, rigour, and originality claims from inside each paper. If my weakest articles are removed, then the correlation with average scores (r=0.200) falls below statistical significance, suggesting that ChatGPT struggles to make fine-grained evaluations.
</sec>
<sec>
<title style='display:none'>Research limitations</title>
The data is self-evaluations of a convenience sample of articles from one academic in one field.
</sec>
<sec>
<title style='display:none'>Practical implications</title>
Overall, ChatGPT does not yet seem to be accurate enough to be trusted for any formal or informal research quality evaluation tasks. Research evaluators, including journal editors, should therefore take steps to control its use.
</sec>
<sec>
<title style='display:none'>Originality/value</title>
This is the first published attempt at post-publication expert review accuracy testing for ChatGPT.
</sec>
</abstract>

Can ChatGPT evaluate research quality?

<abstract>
<title style='display:none'>Abstract</title>
<sec>
<title style='display:none'>Purpose</title>
Recently, global science has shown an increasing open trend, however, the characteristics of research integrity of open access (OA) publications have rarely been studied. The aim of this study is to compare the characteristics of retracted articles across different OA levels and discover whether OA level influences the characteristics of retracted articles.
</sec>
<sec>
<title style='display:none'>Design/methodology/approach</title>
The research conducted an analysis of 6,005 retracted publications between 2001 and 2020 from the Web of Science and Retraction Watch databases. These publications were categorized based on their OA levels, including Gold OA, Green OA, and non-OA. The study explored retraction rates, time lags and reasons within these categories.
</sec>
<sec>
<title style='display:none'>Findings</title>
The findings of this research revealed distinct patterns in retraction rates among different OA levels. Publications with Gold OA demonstrated the highest retraction rate, followed by Green OA and non-OA. A comparison of retraction reasons between Gold OA and non-OA categories indicated similar proportions, while Green OA exhibited a higher proportion due to falsification and manipulation issues, along with a lower occurrence of plagiarism and authorship issues. The retraction time lag was shortest for Gold OA, followed by non-OA, and longest for Green OA. The prolonged retraction time for Green OA could be attributed to an atypical distribution of retraction reasons.
</sec>
<sec>
<title style='display:none'>Research limitations</title>
There is no exploration of a wider range of OA levels, such as Hybrid OA and Bronze OA.
</sec>
<sec>
<title style='display:none'>Practical implications</title>
The outcomes of this study suggest the need for increased attention to research integrity within the OA publications. The occurrences of falsification, manipulation, and ethical concerns within Green OA publications warrant attention from the scientific community.
</sec>
<sec>
<title style='display:none'>Originality/value</title>
This study contributes to the understanding of research integrity in the realm of OA publications, shedding light on retraction patterns and reasons across different OA levels.
</sec>
</abstract>

A comparative study on characteristics of retracted publications across different open access levels

<abstract>
<title style='display:none'>Abstract</title>
<sec>
<title style='display:none'>Purpose</title>
The notable increase in retraction papers has attracted considerable attention from diverse stakeholders. Various sources are now offering information related to research integrity, including concerns voiced on social media, disclosed lists of paper mills, and retraction notices accessible through journal websites. However, despite the availability of such resources, there remains a lack of a unified platform to consolidate this information, thereby hindering efficient searching and cross-referencing. Thus, it is imperative to develop a comprehensive platform for retracted papers and related concerns. This article aims to introduce “Amend,” a platform designed to integrate information on research integrity from diverse sources.
</sec>
<sec>
<title style='display:none'>Design/methodology/approach</title>
The Amend platform consolidates concerns and lists of problematic articles sourced from social media platforms (e.g., PubPeer, For Better Science), retraction notices from journal websites, and citation databases (e.g., Web of Science, CrossRef). Moreover, Amend includes investigation and punishment announcements released by administrative agencies (e.g., NSFC, MOE, MOST, CAS). Each related paper is marked and can be traced back to its information source via a provided link. Furthermore, the Amend database incorporates various attributes of retracted articles, including citation topics, funding details, open access status, and more. The reasons for retraction are identified and classified as either academic misconduct or honest errors, with detailed subcategories provided for further clarity.
</sec>
<sec>
<title style='display:none'>Findings</title>
Within the Amend platform, a total of 32,515 retracted papers indexed in SCI, SSCI, and ESCI between 1980 and 2023 were identified. Of these, 26,620 (81.87%) were associated with academic misconduct. The retraction rate stands at 6.64 per 10,000 articles. Notably, the retraction rate for non-gold open access articles significantly differs from that for gold open access articles, with this disparity progressively widening over the years. Furthermore, the reasons for retractions have shifted from traditional individual behaviors like falsification, fabrication, plagiarism, and duplication to more organized large-scale fraudulent practices, including Paper Mills, Fake Peer-review, and Artificial Intelligence Generated Content (AIGC).
</sec>
<sec>
<title style='display:none'>Research limitations</title>
The Amend platform may not fully capture all retracted and concerning papers, thereby impacting its comprehensiveness. Additionally, inaccuracies in retraction notices may lead to errors in tagged reasons.
</sec>
<sec>
<title style='display:none'>Practical implications</title>
Amend provides an integrated platform for stakeholders to enhance monitoring, analysis, and research on academic misconduct issues. Ultimately, the Amend database can contribute to upholding scientific integrity.
</sec>
<sec>
<title style='display:none'>Originality/value</title>
This study introduces a globally integrated platform for retracted and concerning papers, along with a preliminary analysis of the evolutionary trends in retracted papers.
</sec>
</abstract>

Amend: an integrated platform of retracted papers and concerned papers

<abstract>
<title style='display:none'>Abstract</title>
Research data infrastructures form the cornerstone in both cyber and physical spaces, driving the progression of the data-intensive scientific research paradigm. This opinion paper presents an overview of global research data infrastructure, drawing insights from national roadmaps and strategic documents related to research data infrastructure. It emphasizes the pivotal role of research data infrastructures by delineating four new missions aimed at positioning them at the core of the current scientific research and communication ecosystem. The four new missions of research data infrastructures are: (1) as a pioneer, to transcend the disciplinary border and address complex, cutting-edge scientific and social challenges with problem- and data-oriented insights; (2) as an architect, to establish a digital, intelligent, flexible research and knowledge services environment; (3) as a platform, to foster the high-end academic communication; (4) as a coordinator, to balance scientific openness with ethics needs.
</abstract>

New roles of research data infrastructure in research paradigm evolution

<abstract>
<title style='display:none'>Abstract</title>
<sec>
<title style='display:none'>Purpose</title>
The goal of this study is to analyze the relationship between funded and unfunded papers and their citations in both basic and applied sciences.
</sec>
<sec>
<title style='display:none'>Design/methodology/approach</title>
A power law model analyzes the relationship between research funding and citations of papers using 831,337 documents recorded in the Web of Science database.
</sec>
<sec>
<title style='display:none'>Findings</title>
The original results reveal general characteristics of the diffusion of science in research fields: a) Funded articles receive higher citations compared to unfunded papers in journals; b) Funded articles exhibit a super-linear growth in citations, surpassing the increase seen in unfunded articles. This finding reveals a higher diffusion of scientific knowledge in funded articles. Moreover, c) funded articles in both basic and applied sciences demonstrate a similar expected change in citations, equivalent to about 1.23%, when the number of funded papers increases by 1% in journals. This result suggests, for the first time, that funding effect of scientific research is an invariant driver, irrespective of the nature of the basic or applied sciences.
</sec>
<sec>
<title style='display:none'>Originality/value</title>
This evidence suggests empirical laws of funding for scientific citations that explain the importance of robust funding mechanisms for achieving impactful research outcomes in science and society. These findings here also highlight that funding for scientific research is a critical driving force in supporting citations and the dissemination of scientific knowledge in recorded documents in both basic and applied sciences.
</sec>
<sec>
<title style='display:none'>Practical implications</title>
This comprehensive result provides a holistic view of the relationship between funding and citation performance in science to guide policymakers and R&amp;D managers with science policies by directing funding to research in promoting the scientific development and higher diffusion of results for the progress of human society.
</sec>
</abstract>

General laws of funding for scientific citations: how citations change in funded and unfunded research between basic and applied sciences

<abstract>
<title style='display:none'>Abstract</title>
<sec>
<title style='display:none'>Purpose</title>
The collaboration relationships between innovation actors at a geographic level may be considered as grouping two separate layers, the domestic and the foreign. At the level of each layer, the relationships and the actors involved constitute a Triple Helix game. The paper distinguished three levels of analysis: the global grouping together all actors, the domestic grouping together domestic actors, and the foreign related to only actors from partner countries.
</sec>
<sec>
<title style='display:none'>Design/methodology/approach</title>
Bibliographic records data from the Web of Science for South Korea and West Africa breakdown per innovation actors and distinguishing domestic and international collaboration are analyzed with game theory. The core, the Shapley value, and the nucleolus are computed at the three levels to measure the synergy between actors.
</sec>
<sec>
<title style='display:none'>Findings</title>
The synergy operates more in South Korea than in West Africa; the government is more present in West Africa than in South Korea; domestic actors create more synergy in South Korea, but foreign more in West Africa; South Korea can consume all the foreign synergy, which is not the case of West Africa.
</sec>
<sec>
<title style='display:none'>Research limitations</title>
Research data are limited to publication records; techniques and methods used may be extended to other research outputs.
</sec>
<sec>
<title style='display:none'>Practical implications</title>
West African governments should increase their investment in science, technology, and innovation to benefit more from the synergy their innovation actors contributed at the foreign level. However, the results of the current study may not be sufficient to prove that greater investment will yield benefits from foreign synergies.
</sec>
<sec>
<title style='display:none'>Originality/value</title>
This paper uses game theory to assess innovation systems by computing the contribution of foreign actors to knowledge production at an area level. It proposes an indicator to this end.
</sec>
</abstract>

The Triple Helix of innovation as a double game involving domestic and foreign actors

<abstract>
<title style='display:none'>Abstract</title>
<sec><title style='display:none'>Purpose</title>Digital literacy and related fields have received interests from scholars and practitioners for more than 20 years; nonetheless, academic communities need to systematically review how the fields have developed. This study aims to investigate the research trends of digital literacy and related concepts since the year of 2000, especially in education.</sec>
<sec><title style='display:none'>Design/methodology/approach</title>The current study analyzes keywords, co-authorship, and cited publications in digital literacy through the scientometric method. The journal articles have been retrieved from the WoS (Web of Science) using four keywords: “Digital literacy,” “ICT literacy,” “information literacy,” and “media literacy.” Further, keywords, publications, and co-authorship are examined and further classified into clusters for more in-depth investigation.</sec>
<sec><title style='display:none'>Findings</title>Digital literacy is a multidisciplinary field that widely embraces literacy, ICT, the Internet, computer skill proficiency, science, nursing, health, and language education. The participants, or study subjects, in digital literacy research range from primary students to professionals, and the co-authorship clusters are distinctive by countries in America and Europe.</sec>
<sec><title style='display:none'>Research limitations</title>This paper analyzes one fixed chunk of a dataset obtained by searching for all four keywords at once. Further studies will retrieve the data from diverse disciplines and will trace the change of the leading research themes by time spans.</sec>
<sec><title style='display:none'>Practical implications</title>To shed light on the findings, using customized digital literacy curriculums and technology is critical for learners at different ages to nurture digital literacy according to their learning aims. They need to cultivate their understanding of the social impact of exploiting technology and computational thinking. To increase the originality of digital literacy-related studies, researchers from different countries and cultures may collaborate to investigate a broader range of digital literacy environments.</sec>
<sec><title style='display:none'>Originality/value</title>The present study reviews research trends in digital literacy and related areas by performing a scientometric study to analyze multidimensional aspects in the fields, including keywords, journal titles, co-authorship, and cited publications.</sec>
</abstract>

A Scientometric Study of Digital Literacy, ICT Literacy, Information Literacy, and Media Literacy

<abstract><title style='display:none'>Abstract</title>The “Norwegian Model” attempts to comprehensively cover all the peer-reviewed scholarly literatures in all areas of research in one single weighted indicator. Thereby, scientific production is made comparable across departments and faculties within and between research institutions, and the indicator may serve institutional evaluation and funding. This article describes the motivation for creating the model in Norway, how it was designed, organized and implemented, as well as the effects and experiences with the model. The article ends with an overview of a new type of bibliometric studies that are based on the type of comprehensive national publication data that the Norwegian Model provides.</abstract>

The Norwegian Model in Norway

Editorial and Peer Review Process

Instructions for Authors

Publication Ethics Statement

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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...

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Journal of Data and Information Science (JDIS, formerly Chinese Journal of Library and Information Science), sponsored by the Chinese...