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Register a new userAnalyzing Linguistic Complexity and Scientific Impact
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Chao Lu
Publication date
2019
fields
Informatics Engineering
and research's language is
English
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The number of publications and the number of citations received have become the most common indicators of scholarly success. In this context, scientific writing increasingly plays an important role in scholars scientific careers. To understand the relationship between scientific writing and scientific impact, this paper selected 12 variables of linguistic complexity as a proxy for depicting scientific writing. We then analyzed these features from 36,400 full-text Biology articles and 1,797 full-text Psychology articles. These features were compared to the scientific impact of articles, grouped into high, medium, and low categories. The results suggested no practical significant relationship between linguistic complexity and citation strata in either discipline. This suggests that textual complexity plays little role in scientific impact in our data sets.
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Recent research has found that select scientists have a disproportional share of highly cited papers. Researchers reasoned that this could not have happened if success in science was random and introduced a hidden parameter Q, or talent, to explain this finding. So, the talented high-Q scientists have many high impact papers. Here I show that an upgrade of an old random citation copying model could also explain this finding. In the new model the probability of citation copying is not the same for all papers but is proportional to the logarithm of the total number of citations to all papers of its author. Numerical simulations of the model give results similar to the empirical findings of the Q-factor article.
The rapid development of modern science and technology has spawned rich scientific topics to research and endless production of literature in them. Just like X-ray imaging in medicine, can we intuitively identify the development limit and internal evolution pattern of scientific topic from the relationship of massive knowledge? To answer this question, we collect 71431 seminal articles of topics that cover 16 disciplines and their citation data, and extracts the idea tree of each topic to restore the structure of the development of 71431 topic networks from scratch. We define the Knowledge Entropy (KE) metric, and the contribution of high knowledge entropy nodes to increase the depth of the idea tree is regarded as the basis for topic development. By observing X-ray images of topics, We find two interesting phenomena: (1) Even though the scale of topics may increase unlimitedly, there is an insurmountable cap of topic development: the depth of the idea tree does not exceed 6 jumps, which coincides with the classical Six Degrees of Separation! (2) It is difficult for a single article to contribute more than 3 jumps to the depth of its topic, to this end, the continuing increase in the depth of the idea tree needs to be motivated by the influence relay of multiple high knowledge entropy nodes. Through substantial statistical fits, we derive a unified quantitative relationship between the change in topic depth ${Delta D}^t(v)$ and the change in knowledge entropy over time ${KE}^tleft(vright)$ of the article $v$ driving the increase in depth in the topic: ${Delta D}^t(v) approx log frac{KE^{t}(v)}{left(t-t_{0}right)^{1.8803}}$ , which can effectively portray evolution patterns of topics and predict their development potential. The various phenomena found by scientific x-ray may provide a new paradigm for explaining and understanding the evolution of science and technology.
Using bibliometric data artificially generated through a model of citation dynamics calibrated on empirical data, we compare several indicators for the scientific impact of individual researchers. The use of such a controlled setup has the advantage of avoiding the biases present in real databases, and allows us to assess which aspects of the model dynamics and which traits of individual researchers a particular indicator actually reflects. We find that the simple citation average performs well in capturing the intrinsic scientific ability of researchers, whatever the length of their career. On the other hand, when productivity complements ability in the evaluation process, the notorious $h$ and $g$ indices reveal their potential, yet their normalized variants do not always yield a fair comparison between researchers at different career stages. Notably, the use of logarithmic units for citation counts allows us to build simple indicators with performance equal to that of $h$ and $g$. Our analysis may provide useful hints for a proper use of bibliometric indicators. Additionally, our framework can be extended by including other aspects of the scientific production process and citation dynamics, with the potential to become a standard tool for the assessment of impact metrics.
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Bozhidar Z. Iliev (Institute for Nuclear Research
, Nuclear Energy
, n Bulgarian Academy of Sciences
2013
Problems for evaluation and impact of published scientific works and their authors are discussed. The role of citations in this process is pointed out. Different bibliometric indicators are reviewed in this connection and ways for generation of new bibliometric indices are given. The influence of different circumstances, like self-citations, number of authors, time dependence and publication types, on the evaluation and impact of scientific papers are considered. The repercussion of works citations and their content is investigated in this respect. Attention is paid also on implicit citations which are not covered by the modern bibliometrics but often are reflected in the peer reviews. Some aspects of the Web analogues of citations and new possibilities of the Internet resources in evaluating authors achievements are presented.
Several studies exist which use scientific literature for comparing scientific activities (e.g., productivity, and collaboration). In this study, using co-authorship data over the last 40 years, we present the evolutionary dynamics of multi level (i.e., individual, institutional and national) collaboration networks for exploring the emergence of collaborations in the research field of steel structures. The collaboration network of scientists in the field has been analyzed using author affiliations extracted from Scopus between 1970 and 2009. We have studied collaboration distribution networks at the micro-, meso- and macro-levels for the 40 years. We compared and analyzed a number of properties of these networks (i.e., density, centrality measures, the giant component and clustering coefficient) for presenting a longitudinal analysis and statistical validation of the evolutionary dynamics of steel structures collaboration networks. At all levels, the scientific collaborations network structures were central considering the closeness centralization while betweenness and degree centralization were much lower. In general networks density, connectedness, centralization and clustering coefficient were highest in marco-level and decreasing as the network size grow to the lowest in micro-level. We also find that the average distance between countries about two and institutes five and for authors eight meaning that only about eight steps are necessary to get from one randomly chosen author to another.
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