Please help us test our new pre-print finding feature by giving the pre-print link a rating. A 5 star rating indicates the linked pre-print has the exact same content as the published article.
Please help us test our new pre-print finding feature by giving the pre-print link a rating. A 5 star rating indicates the linked pre-print has the exact same content as the published article.
Please help us test our new pre-print finding feature by giving the pre-print link a rating. A 5 star rating indicates the linked pre-print has the exact same content as the published article.
Please help us test our new pre-print finding feature by giving the pre-print link a rating. A 5 star rating indicates the linked pre-print has the exact same content as the published article.
Abstract: Abstract No study has so far examined retractions in primary care. Our aim was to assess the number/proportion of retracted articles in primary care journals and describe their main characteristics. For comparison, we also calculated the number/proportion of retractions for general internal medicine journals and for all PubMed articles. We selected the eighteen primary care journals with Journal Citation Reports (JCR) impact factor in 2021. We retrieved all PubMed articles published in these journals between January 2000 and December 2022 that were retracted. We calculated the proportion of retractions by dividing the number of retractions by the number of PubMed articles published in these journals during the same period. We also calculated the proportion of retractions for (i) all PubMed articles published in the 117 general internal medicine journals with a JCR impact factor > 2 in 2021 and (ii) all PubMed articles. We found seven retractions among the 52,453 PubMed articles published in the eighteen primary care journals. The proportion of retractions (= 0.013%) was about two times lower than for articles published in internal medicine journals (= 0.028%) and about four times lower than for all PubMed articles (= 0.056%). Four articles were retracted for misconduct, two for unintentional errors and one for another reason. Although it may be explained by a particularly high level of scientific rigour and integrity among primary care researchers, the low number of retractions in primary care journals raises questions about the effectiveness of retraction measures in these journals. PubDate: 2023-12-01
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Abstract: Abstract Citations play a significant role in the evaluation of scientific literature and researchers. Citation intent analysis is essential for academic literature understanding. Meanwhile, it is useful for enriching semantic information representation for the citation intent classification task because of the rapid growth of publicly accessible full-text literature. However, some useful information that is readily available in citation context and facilitates citation intent analysis has not been fully explored. Furthermore, some deep learning models may not be able to learn relevant features effectively due to insufficient training samples of citation intent analysis tasks. Multi-task learning aims to exploit useful information between multiple tasks to help improve learning performance and exhibits promising results on many natural language processing tasks. In this paper, we propose a joint semantic representation model, which consists of pretrained language models and heterogeneous features of citation intent texts. Considering the correlation between citation intents, citation section and citation worthiness classification tasks, we build a multi-task citation classification framework with soft parameter sharing constraint and construct independent models for multiple tasks to improve the performance of citation intent classification. The experimental results demonstrate that the heterogeneous features and the multi-task framework with soft parameter sharing constraint proposed in this paper enhance the overall citation intent classification performance. PubDate: 2023-12-01
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Abstract: Abstract How science contributes to technological innovation can benefit from a deep understanding of intrinsic characteristics of the science base that underlie technologies, especially characteristics with significant implications for the scientific base itself. This paper investigates the correlation between interdisciplinarity of scientific research (variety, balance, disparity, and Rao-Stirling) and their technological impact. Using all Web of Science research articles published in 2002 and USPTO patents, we find that the likelihood of a paper being cited by patents increases with variety and Rao-Stirling, and decreases with balance and disparity. Regarding specific technological impact, the significance of interdisciplinarity is more prominent in the long term and exhibits variations among different disciplines. Specifically, the intensity of technical impact decreases at a decreasing rate with variety over time, increases at a decreasing rate with Rao-Stirling over time, and decreases with disparity in the long term. Balance is insignificant but it presents a positive correlation in medicine and a negative correlation in natural science in the long term. The scope of technological impact focuses on the number of claims and IPCs, increase with variety and disparity in the long term, and increase with balance in the short term, but such positive correlation only in natural science in the long term. Furthermore, scientific impact and technological impact are closely related in our study, but in order to have technological impacts, interdisciplinary papers need first to reach a certain threshold in scientific impact. Our findings suggest that what is considered excellent within interdisciplinary research can potentially lead to remarkable advancements in technological innovation. PubDate: 2023-12-01
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Abstract: Abstract This study conducted a comparative analysis of scientific mobility by using curricula vitae (CV) and bibliometric analyses. This study analyzed data from the CVs and publications of 731 recipients of the Sloan Research Fellowship in Mathematics. The results revealed substantial differences in the prevalence of researchers with and without temporary institutional affiliations. Based on discrepancies in the number and names of institutions obtained from CV and publication analyses, researchers were categorized into 14 groups. The results of CV data and publication analyses were the same for only 6.7% of researchers. To address these disparities, adjustments were made to the numbers of home and temporary institutions for each researcher by using their CVs to accurately determine their actual affiliations. Notably, corrections were required for the majority of recipients in terms of the numbers of home and temporary institutions based on publication data, highlighting the importance of CVs in this context. This study identified several factors necessitating such corrections. Given the limitations of CV and bibliometric analyses, this study recommends combining both methodologies for comprehensive scientific mobility research. PubDate: 2023-12-01
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Abstract: Abstract Despite the progress made in terms of equality, there is still significant underrepresentation of women in higher education institutions, particularly in science and technology-oriented universities. The aim of this paper is to measure the efficiency of the research activity undertaken in a Spanish technological university, with a focus on the distribution by gender in the different disciplines. First, a non-concave metafrontier is estimated, which allows the introduction of different researcher profiles, by major scientific fields, and can be used to identify not only the areas that demonstrate better management of their research, development and innovation activity, but also the relative position of women scientists. Second, the cross-efficiency method is employed to construct a synthetic index, which in turn is used to establish a ranking of each of the knowledge areas that make up the analysed fields. The results show that women slightly outperform men in terms of research efficiency. University research support policies that apply efficiency criteria as the key to the distribution of grants rather than performance measured in terms of the volume of research output would improve the situation of women scientists and the incentives provided. PubDate: 2023-12-01
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Abstract: Abstract This study aims to investigate and identify the driving factors of word co-occurrence from the perspective of semantic relations between frequently co-occurring words. Natural sentences in a corpus of news articles were used as co-occurrence windows to extract co-occurring word pairs, and the distance of those two words was not limited. ConceptNet (a semantic knowledge base) was used to annotate the semantic relation between co-occurring words. To solve the problem that some co-occurring word pairs fail to match direct semantic relations in ConceptNet, we proposed a relation annotation method by connecting them with an intermediate word. Results showed that six semantic relations in ConceptNet, (i.e., RelatedTo, IsA, Synonym, HasContext, Antonym, and MannerOf) were important factors directly inducing word co-occurrence. The combination of some of those semantic relations was an important factor indirectly driving word co-occurrence. Also, syntactic analysis and lexical semantic theories were combined to analyze the direct and indirect semantic relations. In this analysis, we found that the factors driving word co-occurrence in sentences could be classified into three relation categories: collocation and modification, hyponymy, and synonym and antonym. These findings can help explain the phenomenon of word co-occurrence and improve the method and application of co-word analysis. PubDate: 2023-12-01
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Abstract: Abstract This paper presents the extent of retractions in scientific publications of BRICS countries indexed in the SCOPUS database from 1989 to 2021. The scientific publication of BRICS countries has increased in recent years, which has given way to an increase in retraction. However, much money is being invested in research and development work resulting in more publications by BRICS than any other developing country. Out of 11,764 retracted publications, 72.67% of papers are written by two–five authors, in that 230 retracted articles are published by top 20 authors, it sums up to 1.99%. Retraction on conference proceedings share is 77.90% in that IEEE Computing Society retracts 99.04% of papers. Springer publisher retracts 29.69% journal articles. In the source of journal publication the Arabian Journal of Geosciences has 6.19% retractions. Out of 689 retraced citations, 40.64% are post-citations, and 59.36% are pre-citations. Three articles noticed self-citations in pre-retracted publications but no self-citations in the top 10 post-retracted articles. Unless the academic and research communities enforce severe action to address the growing problems of fabrication and plagiarism, research will be futile. More retraction would result in the degradation of the authors as well as the institution; both have to join hands and follow the publication ethics to stop or at least avoid retraction in the future. PubDate: 2023-12-01
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Abstract: Abstract This study uncovers the internationalization of Chinese social sciences in knowledge sources through the quantitative analysis of foreign language references cited by China’s domestically published social science papers. It’s found that in the 20 years from 1998 to 2017, the number and proportion of international knowledge sources utilized in Chinese social sciences increased greatly, and the distribution of languages became more concentrated in English. For journals as major international knowledge sources, Chinese social scientists consistently preferred those indexed by Web of Science, and their citation pattern conforms to Garfield’s Law of Concentration. Some of the disciplines continuously focused on international journals in their fields, but others increasingly utilized interdisciplinary knowledge. The strong demand of Chinese social scientists for journals with high impact was basically stable over 20 years, while older articles instead of recent ones were preferred generally and increasingly. On the basis of the above findings, this study further discloses that China’s knowledge production in social sciences also experienced rapid internationalization like its knowledge utilization, and the association between international knowledge production and utilization is statistically significant at the discipline level and journal level. Finally, policy-related issues and enlightenments for social sciences in non-English speaking countries are discussed. PubDate: 2023-12-01
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Abstract: The wide range and enormous volume of academic papers on the Internet prompted researchers to recommend models that could provide users with customized academic article recommendations. Nevertheless, previous approaches struggled with “sparsity” and “cold-start” as a consequence of a lack of sufficient information about research articles. Furthermore, they fail to recognize the importance of important factors and long-range dependencies, thus restricting their ability to make reliable and reasonable recommendations. To address these issues, we suggest RAR-SB, a research article recommender model that uses a pre-trained language model for scientific text named SciBERT to learn context-preserving research article representations. To learn the researcher’s preferences, the model exploits semantics corresponding to the title, abstract, authors, and field of study(FoS)/keywords of the candidate and query papers. The model captures long-range dependencies and salient features using the BiGRU network and the attention module, respectively. The experimental findings on the DBLP-V12 dataset demonstrate that the suggested recommendation model outperforms the baseline approaches regarding mean reciprocal rank (MRR) and mean average precision (MAP) by nearly 3.7% and 5.3%, respectively. Similarly, on the DBLP-V13 dataset, the proposed model has improved 6% and 5% better MRR and MAP results, respectively. PubDate: 2023-12-01
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Abstract: Abstract This study analyzes the publication requirements of PhD programs in China. It is based on a representative sample of PhD programs from 164 Chinese universities from all fields of science. Our results show that Chinese PhD student significant pressures to publish in order to obtain their degree, with papers indexed in the Science Citation Index often a mandatory requirement for students to obtain their degree. Moreover, it is found that first authorship is also mandatory: only as first authors count towards the degree, which may affect PhD students’ collaborative behavior. These findings highlight the role of publications indexed in the Science Citation Index for China’s PhD programs and contributes to our understanding of the landscape of research evaluation in China’s higher education system. PubDate: 2023-12-01
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Abstract: Abstract Knowledge absorption and integration between research domains can generate new concepts and ideas, and cross-domain research cooperation has become an effective way to promote innovation. Observing and discovering the patterns and trends in the development of cross-domain collaborative research topics is an important area of research for promoting the innovative development of disciplines. In this paper, we propose an evolution analysis method for cross-domain collaborative research topics, which first employs LDA and Word2vec models to extract topics from the domain corpus, and proposes a cross-domain topic evolution model (CDTEM) based on cross-time and cross-domain topic associations. Based on CDTEM, combined with the evolution analysis strategy of forward extrapolation and backward tracking, the method realizes the evolution analysis of cross-domain topics (CDTs) and generates a synergistic evolution vein of CDTs. Finally, we combine the integration and evolution of research topics in conceptual design (CD) and design cognition (DC) to perform validation analysis. The methodology of this paper provides a new perspective for studying interdisciplinary topic convergence trends based on collaborative goal-oriented research, which can help scholars capture the convergence and development trends of cross-domain collaborative research topics over the years and explore dynamic CDTs to effectively support interdisciplinary scientific exchange. At the same time, the case study part of this paper provides scholars conducting research in cognition-based product design with a scientific analysis of the integration and development of research topics. PubDate: 2023-11-10
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Abstract: Abstract National Science and Technology Council (NSTC) of Republic of China (Taiwan) launched a program named the Columbus Program in 2017 for university professors younger than thirty-five (extended to thirty-eight one year later) to apply for research grant to explore the unknown. People are curious about how the eligible age thirty-eight was determined. To discuss whether this age is reasonable, this paper investigates the most productive age of scholars in academic institutions in Taiwan, focusing on the field of management. The productivity is represented by the number of papers published in scientific journals. Based on a sample of 4,413 management scholars, the most productive age is found to appear at forty, which is close to thirty-eight, the eligible age stipulated by NSTC. Female scholars publish twenty-three percent less of papers per person than male scholars and scholars of private institutions publish thirty-six percent less of papers than scholars of public institutions. However, their most productive ages are similar. The results also show that scholars of older generations have lower productivity and their most productive age appears later than that of younger generations. An average productivity analysis is also conducted. The results show that management scholars develop their research capability in the first twenty years of their career life, and the capability remains at a similar level until they retire. PubDate: 2023-11-09
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Abstract: Abstract An overall rise in the citation parameters used in the metrics of scientific publications (i.e. journal impact factor, JIF) has taken place since the last decade of the previous century, coinciding with the electronic distribution of (and access to) scientific literature. This inflation like tendency is herein analyzed in the area of Materials Science and also affects the number of publications. Considering average JIF values, its growth is proportional to the number of publications in the area and to its JIF value, leading to an inhomogeneous boost that preferentially benefits those journals with high JIF. An elevation in the number of publications per year alone cannot explain this behavior but it occurs due to a continuous and widespread increment in the number of citations per article, which only remains limited when restrictions are applied by journals to the maximum number of pages per article. In this work we observe this positive correlation between the increase in the number of references per article and the overall increase in JIF but, in our analysis, a kink point is observed in consistency with the appearance of online databases, particularly those free available in 2004. Online databases along with the widespread of open access publishing option made the research content easily available to the scientific community contributing to an increasing trend (without apparent saturation) in the number of articles used to contextualize the new scientific contributions. PubDate: 2023-11-07
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Abstract: Background Evaluating academics is a challenge, and the use of indicators such as scientific impact (i.e. number of published papers and their citation rate) is complex and poorly validated. We propose a new indicator for academic medical research: the “Free lunches” index (fl-index), computed from the sum of gifts from the industry. The fl-index provides a direct and straightforward measure of industry investment consisting in regaling a clinical researcher with rewards like a leisurely meal in a Michelin-starred restaurant or a relaxing stay in a high-end resort hotel. Methods and findings 3,936 French academics were included in this observational and satirical retrospective study using the French database registering gifts received by medical doctors and Web of Science, over the years 2014–2019. Pearson’s correlation coefficients explored the associations between the fl-index and in the h-index (the maximum number of published papers h that have each been cited at least h times) increase over the period 2014–2019. The diagnostic properties and optimal thresholds of the fl-index for detecting high scientific productivity were explored. High scientific productivity was defined as ranking in the top 25% scientists in terms of increase in the h-index. To detect possible differences according to medical disciplines, subgroup analyses were performed. The correlation coefficient between the fl-index and the increase in the h-index was 0.31 (95% CI 0.29 to 0.34). The optimal threshold was 7,700 € for the fl-index, giving a sensitivity of 65% (95% CI 61 to 67%), a specificity of 59% (95% CI 57 to 61%). However, there were considerable differences across medical disciplines, with correlations ranging from 0.12 (Morphology and morphogenesis) to 0.51 (Internal medicine, geriatrics, general surgery and general medicine), and the median fl-index ranging from 37 € (Public health, environment and society) to 30 404 € (Cardiorespiratory and vascular pathologies). Importantly, the highest correlations and values for the fl-index were observed for clinical disciplines. Conclusions Overall, the correlation between the fl-index and an increase in the h-index was modest so that the fl-index cannot be used as a surrogate for academic success as gauged by productivity-based metrics. However, future residents could use these results to complement the usual metrics in order to choose (or avoid) certain specialties, depending on whether they are more eager to produce scientific articles or to enjoy an affluent lifestyle that they consider well-deserved. Registration osf.io/7d4bk. PubDate: 2023-11-06
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Abstract: Abstract The Matthew effect is widely used by researchers across disciplines. However, few studies have focused on this effect’s magnitude variation on the background of the open access movement and expanded avenues to obtain information. Citation is the most widespread and basic form of scholarly recognition in the reward system of science, therefore, scientists are motivated to refer to the work of their peers where reference is due. This study assumes that the Matthew effect may not play a major role in science anymore and uses citations as a proxy to measure this effect, and calculates the citation fluctuation of Noble Laureates’ key publications before and after winning the award during 1901–2016. The results show that the coefficient of variation of citations is smaller for publications published after 1980 than for those published before. The median of citations in chemistry is higher than that for in physics, physiology, or medicine. Additionally, over 90% of publications published after 1980 were recognized by their community pre-award, while the ratio consisted of 84% and 75% for 1940–1980 and 1900–1940, respectively. Furthermore, the time range between publication and year awarded plays a role in this phenomenon. The study suggests a potential magnitude decrease in the Matthew effect, which is a reminder that most researchers nowadays will recognize the importance of scientific breakthrough in its early stage. PubDate: 2023-11-04
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Abstract: Abstract With the exponential increase of interdisciplinary research, identifying accurate disciplines of scientific documents has become increasingly important in various research management tasks. Interdisciplinary classification, which classifies documents into multiple disciplines, is essential for multidisciplinary research development. Due to the scarcity of labeled multidiscipline data, existing scientific document classification methods can't solve the interdisciplinary issue. Most of them also have the problem of explainability with curtly providing classification results. This study proposes an explainable transfer-learning-based classification method for interdisciplinary documents. First, we trained a single-discipline classification model using existing labeled single-discipline documents. Then, we transfer the knowledge learned from single-discipline classification to interdisciplinary classification to address the scarcity of labeled interdisciplinary data. We also added discipline co-occurrence information into our proposed model. Finally, we obtained our final model by training the transferred model with interdisciplinary data. In addition, keyword-based explanations for classifying texts are provided by employing layer-wise relevance propagation. Experiments on real-life NSFC data show the effectiveness of the proposed method, which can promote interdisciplinary development by constructing an efficient and fair classification for interdisciplinary review systems. PubDate: 2023-11-04
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Abstract: Abstract Research metrics are known to predict many markers of scientific eminence, but fellowship in learned academies has not been examined in this context. The present research used Scopus-based citation indices, including a composite index developed by Ioannidis et al., (PLoS Biol 14:e1002501, 2016, https://doi.org/10.1371/journal.pbio.1002501) that improves cross-field comparison, to predict fellowship in the Australian Academy of Sciences (AAS). Based on ideas of a hierarchy of the sciences, the study also examined whether researchers from natural science fields were advantaged in achieving AAS fellowship relative to researchers from fields toward the social science end of the hierarchy. In a comprehensive sample of top global researchers, the composite index and its components all strongly differentiated Australian researchers who were elected as AAS fellows from those who were not. As predicted, when composite index scores were statistically controlled, researchers in physical and mathematical sciences were more likely to achieve fellow status than biological scientists, who were much more likely to achieve it than psychological, cognitive, and social scientists. Researchers in basic science fields also had an election advantage over those in more applied and technological fields. These findings suggest that recognition by learned academies may be predicted by citation indices, but may also be influenced by the perceived hardness, prestige, and purity of research fields. PubDate: 2023-11-04
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