共查询到20条相似文献，搜索用时 31 毫秒
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《Journal of Informetrics》2014,8(2):421-430
Unlike Impact Factors (IF), Article Influence (AI) scores assign greater weight to citations that appear in highly cited journals. The natural sciences tend to have higher citation rates than the social sciences. We might therefore expect that relative to IF, AI overestimates the citation impact of social science journals in subfields that are related to (and presumably cited in) higher-impact natural science disciplines. This study evaluates that assertion through a set of simple and multiple regressions covering seven social science disciplines: anthropology, communication, economics, education, library and information science, psychology, and sociology. Contrary to expectations, AI underestimates 5IF (five-year Impact Factor) for journals in science-related subfields such as scientific communication, science education, scientometrics, biopsychology, and medical sociology. Journals in these subfields have low AI scores relative to their 5IF values. Moreover, the effect of science-related status is considerable—typically 0.60 5IF units or 0.50 SD. This effect is independent of the more general finding that AI scores underestimate 5IF for higher-impact journals. It is also independent of the very modest curvilinearity in the relationship between AI and 5IF. 相似文献
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《Journal of Informetrics》2014,8(2):318-328
Citation based approaches, such as the impact factor and h-index, have been used to measure the influence or impact of journals for journal rankings. A survey of the related literature for different disciplines shows that the level of correlation between these citation based approaches is domain dependent. We analyze the correlation between the impact factors and h-indices of the top ranked computer science journals for five different subjects. Our results show that the correlation between these citation based approaches is very low. Since using a different approach can result in different journal rankings, we further combine the different results and then re-rank the journals using a combination method. These new ranking results can be used as a reference for researchers to choose their publication outlets. 相似文献
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Henk F. Moed 《Journal of Informetrics》2010,4(3):265-277
This paper explores a new indicator of journal citation impact, denoted as source normalized impact per paper (SNIP). It measures a journal's contextual citation impact, taking into account characteristics of its properly defined subject field, especially the frequency at which authors cite other papers in their reference lists, the rapidity of maturing of citation impact, and the extent to which a database used for the assessment covers the field's literature. It further develops Eugene Garfield's notions of a field's ‘citation potential’ defined as the average length of references lists in a field and determining the probability of being cited, and the need in fair performance assessments to correct for differences between subject fields. A journal's subject field is defined as the set of papers citing that journal. SNIP is defined as the ratio of the journal's citation count per paper and the citation potential in its subject field. It aims to allow direct comparison of sources in different subject fields. Citation potential is shown to vary not only between journal subject categories – groupings of journals sharing a research field – or disciplines (e.g., journals in mathematics, engineering and social sciences tend to have lower values than titles in life sciences), but also between journals within the same subject category. For instance, basic journals tend to show higher citation potentials than applied or clinical journals, and journals covering emerging topics higher than periodicals in classical subjects or more general journals. SNIP corrects for such differences. Its strengths and limitations are critically discussed, and suggestions are made for further research. All empirical results are derived from Elsevier's Scopus. 相似文献
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《Journal of Informetrics》2014,8(2):340-348
In the present paper the Percentage Rank Position (PRP) index concluding from the principle of Similar Distribution of Information Impact in different fields of science (Vinkler, 2013), is suggested to assess journals in different research fields comparatively. The publications in the journals dedicated to a field are ranked by citation frequency, and the PRP-index of the papers in the elite set of the field is calculated. The PRP-index relates the citation rank number of the paper to the total number of papers in the corresponding set. The sum of the PRP-index of the elite papers in a journal, PRP(j,F) may represent the eminence of the journal in the field. The non-parametric and non-dimensional PRP(j,F) index of journals is believed to be comparable across fields. 相似文献
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《Journal of Informetrics》2023,17(3):101425
Rousseau and Mutz argued that the existing researches on diversity measure methods, such as the Rao-Stirling index, DIV, etc., have shortcomings, and urged colleagues to find a better framework for diversity measure. Based on Shannon entropy and entropy of degree vector sum, in this contribution a new diversity measure EDVS (Entropy of Degree Vectors Sum) is proposed, which meets all requirements of variety, balance and disparity, and can directly calculate the value of diversity from the observed sample data without calculating the joint probability distribution of two random variables, or mutual information. The empirical results show that: (1) the ranking of the EDVS measure has a higher Spearman correlation coefficient with DIV and DIV* than with Shannon entropy. (2) The EDVS ranking is more relevant with DIV* than with DIV. (3) The diversity of soft science journals is higher than that of hard science journals, which indicates that the interdisciplinary research of social sciences and humanities is more common than that of hard sciences such as sciences and engineering sciences. (4) Rao-Stirling index and DIV index are more sensitive to sample size. The computational complexity of the Rao-Stirling index and DIV index is , while the computational complexity of the EDVS index is . This provides the feasibility for analyzing high-dimension networks and large data sets. Results of verification on different types of data sets show that EDVS can not only effectively measure the diversity of disciplines in interdisciplinary research, but also effectively measure the diversity of other entities. 相似文献
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《Journal of Informetrics》2014,8(1):136-146
The non-citation rate refers to the proportion of papers that do not attract any citation over a period of time following their publication. After reviewing all the related papers in Web of Science, Google Scholar and Scopus database, we find the current literature on citation distribution gives more focus on the distribution of the percentages and citations of papers receiving at least one citation, while there are fewer studies on the time-dependent patterns of the percentage of never-cited papers, on what distribution model can fit their time-dependent patterns, as well as on the factors influencing the non-citation rate. Here, we perform an empirical pilot analysis to the time-dependent distribution of the percentages of never-cited papers in a series of different, consecutive citation time windows following their publication in our selected six sample journals, and study the influence of paper length on the chance of papers’ getting cited. Through the above analysis, the following general conclusions are drawn: (1) a three-parameter negative exponential model can well fit time-dependent distribution curve of the percentages of never-cited papers; (2) in the initial citation time window, the percentage of never-cited papers in each journal is very high. However, as the citation time window becomes wider and wider, the percentage of never-cited papers begins to drop rapidly at first, and then drop more slowly, and the total degree of decline for most of journals is very large; (3) when applying the wider citation time windows, the percentage of never-cited papers for each journal begins to approach a stable value, and after that value, there will be very few changes in these stable percentages, unless we meet a large amount of “Sleeping Beauties” type papers; (4) the length of an paper has a great influence on whether it will be cited or not. 相似文献