How citation boosts promote scientific paradigm shifts and Nobel Prizes

Nobel Prizes are commonly seen to be among the most prestigious achievements of our times. Based on mining several million citations, we quantitatively analyze the processes driving paradigm shifts in science. We find that groundbreaking discoveries of Nobel Prize Laureates and other famous scientists are not only acknowledged by many citations of their landmark papers. Surprisingly, they also boost the citation rates of their previous publications. Given that innovations must outcompete the rich-gets-richer effect for scientific citations, it turns out that they can make their way only through citation cascades. A quantitative analysis reveals how and why they happen. Science appears to behave like a self-organized critical system, in which citation cascades of all sizes occur, from continuous scientific progress all the way up to scientific revolutions, which change the way we see our world. Measuring the "boosting effect" of landmark papers, our analysis reveals how new ideas and new players can make their way and finally triumph in a world dominated by established paradigms. The underlying "boost factor" is also useful to discover scientific breakthroughs and talents much earlier than through classical citation analysis, which by now has become a widespread method to measure scientific excellence, influencing scientific careers and the distribution of research funds. Our findings reveal patterns of collective social behavior, which are also interesting from an attention economics perspective. Understanding the origin of scientific authority may therefore ultimately help to explain, how social influence comes about and why the value of goods depends so strongly on the attention they attract.Comment: 6 pages, 6 figure

Recent ASA presidents and ‘top’ journals: observed publication patterns, alleged cartels and varying careers

It has been common for studies presented as about American sociology as a whole to rely on data compiled from leading journals (American Sociological Review [ASR] and American Journal of Sociology [AJS]), or about presidents of the American Sociological Association [ASA], to represent it. Clearly those are important, but neither can be regarded as providing a representative sample of American sociology. Recently, Stephen Turner has suggested that dominance in the ASA rests with a ‘cartel’ initially formed in graduate school, and that it favors work in a style associated with the leading journals. The adequacy of these ideas is examined in the light of available data on the last 20 years, which show that very few of the presidents were in the same graduate schools at the same time. All presidents have had distinguished academic records, but it is shown that their publication strategies have varied considerably. Some have had no ASR publications except their presidential addresses, while books and large numbers of other journals not normally mentioned in this context have figured in their contributions, as well as being more prominent in citations. It seems clear that articles in the leading journals have not been as closely tied to prestigious careers as has sometimes been suggested, and that if there is a cartel it has not included all the presidents

Author Self-Citation in the General Medicine Literature

Background: Author self-citation contributes to the overall citation count of an article and the impact factor of the journal in which it appears. Little is known, however, about the extent of self-citation in the general clinical medicine literature. The objective of this study was to determine the extent and temporal pattern of author self-citation and the article characteristics associated with author self-citation. Methodology/Principal Findings: We performed a retrospective cohort study of articles published in three high impact general medical journals (JAMA, Lancet, and New England Journal of Medicine) between October 1, 1999 and March 31, 2000. We retrieved the number and percentage of author self-citations received by the article since publication, as of June 2008, from the Scopus citation database. Several article characteristics were extracted by two blinded, independent reviewers for each article in the cohort and analyzed in multivariable linear regression analyses. Since publication, author self-citations accounted for 6.5 % (95 % confidence interval 6.3–6.7%) of all citations received by the 328 articles in our sample. Selfcitation peaked in 2002, declining annually thereafter. Studies with more authors, in cardiovascular medicine or infectious disease, and with smaller sample size were associated with more author self-citations and higher percentage of author selfcitation (all p#0.01). Conclusions/Significance: Approximately 1 in 15 citations of articles in high-profile general medicine journals are autho

Researchers’ publication patterns and their use for author disambiguation

Over the recent years, we are witnessing an increase of the need for advanced bibliometric indicators on individual researchers and research groups, for which author disambiguation is needed. Using the complete population of university professors and researchers in the Canadian province of Québec (N=13,479), of their papers as well as the papers authored by their homonyms, this paper provides evidence of regularities in researchers’ publication patterns. It shows how these patterns can be used to automatically assign papers to individual and remove papers authored by their homonyms. Two types of patterns were found: 1) at the individual researchers’ level and 2) at the level of disciplines. On the whole, these patterns allow the construction of an algorithm that provides assignation information on at least one paper for 11,105 (82.4%) out of all 13,479 researchers—with a very low percentage of false positives (3.2%)

Author disambiguation using multi-aspect similarity indicators

Key to accurate bibliometric analyses is the ability to correctly link individuals to their corpus of work, with an optimal balance between precision and recall. We have developed an algorithm that does this disambiguation task with a very high recall and precision. The method addresses the issues of discarded records due to null data fields and their resultant effect on recall, precision and F-measure results. We have implemented a dynamic approach to similarity calculations based on all available data fields. We have also included differences in author contribution and age difference between publications, both of which have meaningful effects on overall similarity measurements, resulting in significantly higher recall and precision of returned records. The results are presented from a test dataset of heterogeneous catalysis publications. Results demonstrate significantly high average F-measure scores and substantial improvements on previous and stand-alone techniques

Are Mendeley Reader Counts Useful Impact Indicators in all Fields?

Reader counts from the social reference sharing site Mendeley are known to be valuable for early research evaluation. They have strong correlations with citation counts for journal articles but appear about a year before them. There are disciplinary differences in the value of Mendeley reader counts but systematic evidence is needed at the level of narrow fields to reveal its extent. In response, this article compares Mendeley reader counts with Scopus citation counts for journal articles from 2012 in 325 narrow Scopus fields. Despite strong positive correlations in most fields, averaging 0.671, the correlations in some fields are as weak as 0.255. Technical reasons explain most weaker correlations, suggesting that the underlying relationship is almost always strong. The exceptions are caused by unusually high educational or professional use or topics of interest within countries that avoid Mendeley. The findings suggest that if care is taken then Mendeley reader counts can be used for early citation impact evidence in almost all fields and for related impact in some of the remainder. As an additional application of the results, cross-checking with Mendeley data can be used to identify indexing anomalies in citation databases