The Research Space: using the career paths of scholars to predict the evolution of the research output of individuals, institutions, and nations

In recent years scholars have built maps of science by connecting the academic fields that cite each other, are cited together, or that cite a similar literature. But since scholars cannot always publish in the fields they cite, or that cite them, these science maps are only rough proxies for the potential of a scholar, organization, or country, to enter a new academic field. Here we use a large dataset of scholarly publications disambiguated at the individual level to create a map of science-or research space-where links connect pairs of fields based on the probability that an individual has published in both of them. We find that the research space is a significantly more accurate predictor of the fields that individuals and organizations will enter in the future than citation based science maps. At the country level, however, the research space and citations based science maps are equally accurate. These findings show that data on career trajectories-the set of fields that individuals have previously published in-provide more accurate predictors of future research output for more focalized units-such as individuals or organizations-than citation based science maps

The Extraction of Community Structures from Publication Networks to Support Ethnographic Observations of Field Differences in Scientific Communication

The scientific community of researchers in a research specialty is an important unit of analysis for understanding the field specific shaping of scientific communication practices. These scientific communities are, however, a challenging unit of analysis to capture and compare because they overlap, have fuzzy boundaries, and evolve over time. We describe a network analytic approach that reveals the complexities of these communities through examination of their publication networks in combination with insights from ethnographic field studies. We suggest that the structures revealed indicate overlapping sub- communities within a research specialty and we provide evidence that they differ in disciplinary orientation and research practices. By mapping the community structures of scientific fields we aim to increase confidence about the domain of validity of ethnographic observations as well as of collaborative patterns extracted from publication networks thereby enabling the systematic study of field differences. The network analytic methods presented include methods to optimize the delineation of a bibliographic data set in order to adequately represent a research specialty, and methods to extract community structures from this data. We demonstrate the application of these methods in a case study of two research specialties in the physical and chemical sciences.Comment: Accepted for publication in JASIS

References made and citations received by scientific articles

This paper studies massive evidence about references made and citations received after a five-year citation window by 3.7 million articles published in 1998-2002 in 22 scientific fields. We find that the distributions of references made and citations received share a number of basic features across sciences. Reference distributions are rather skewed to the right, while citation distributions are even more highly skewed: the mean is about 20 percentage points to the right of the median, and articles with a remarkable or outstanding number of citations represent about 9% of the total. Moreover, the existence of a power law representing the upper tail of citation distributions cannot be rejected in 17 fields whose articles represent 74.7% of the total. Contrary to the evidence in other contexts, the value of the scale parameter is above 3.5 in 13 of the 17 cases. Finally, power laws are typically small but capture a considerable proportion of the total citations received.

The skewness of science in 219 sub-fields and a number of aggregates

This paper studies evidence from Thomson Scientific about the citation process of 3.7 million articles published in the period 1998-2002 in 219 Web of Science categories, or sub-fields. Reference and citation distributions have very different characteristics across sub-fields. However, when analyzed with the Characteristic Scores and Scales technique, which is size and scale independent, the shape of these distributions appear extraordinarily similar. Reference distributions are mildly skewed, but citation distributions with a five-year citation window are highly skewed: the mean is twenty points above the median, while 9-10% of all articles in the upper tail account for about 44% of all citations. The aggregation of sub-fields into disciplines and fields according to several aggregation schemes preserve this feature of citation distributions. On the other hand, for 140 of the 219 sub-fields the existence of a power law cannot be rejected. However, contrary to what is generally believed, at the sub-field level the scaling parameter is above 3.5 most of the time, and power laws are relatively small: on average, they represent 2% of all articles and account for 13.5% of all citations. The results of the aggregation into disciplines and fields reveal that power law algebra is a subtle phenomenon.

The Quest for Citations: Drivers of Article Impact

Why do some articles become building blocks for future scholars, while many others remain unnoticed? We aim to answer this question by contrasting, synthesizing and simultaneously testing three scientometric perspectives – universalism, social constructivism and presentation – on the influence of article and author characteristics on article citations. To do so, we study all articles published in a sample of five major journals in marketing from 1990 to 2002 that are central to the discipline. We count the number of citations each of these articles has received and regress this count on an extensive set of characteristics of the article (i.e. article quality, article domain, title length, the use of attention grabbers and expositional clarity), and the author (i.e. author visibility and author personal promotion). We find that the number of citations an article in the marketing discipline receives, depends upon “what one says†(quality and domain), on “who says it†(author visibility and personal promotion) and not so much on “how one says it†(title length, the use of attention grabbers, and expositional clarity). Our insights contribute to the marketing literature and are relevant to scientific stakeholders, such as the management of scientific journals and individual academic scholars, as they strive to maximize citations. They are also relevant to marketing practitioners. They inform practitioners on characteristics of the academic journals in marketing and their relevance to decisions they face. On the other hand, they also raise challenges towards making our journals accessible and relevant to marketing practitioners: (1) authors visible to academics are not necessarily visible to practitioners; (2) the readability of an article may hurt academic credibility and impact, while it may be instrumental in influencing practitioners; (3) it remains questionable whether articles that academics assess to be of high quality are also managerially relevant.Impact;Citation Analysis;Referencing;Scientometrics;Cite

The skewness of science in 219 sub-fields and a number of aggregates

This paper studies evidence from Thomson Scientific about the citation process of 3.7 million articles published in the period 1998-2002 in 219 Web of Science categories, or sub-fields. Reference and citation distributions have very different characteristics across sub-fields. However, when analyzed with the Characteristic Scores and Scales technique, which is replication and scale invariant, the shape of these distributions over three broad categories of articles appears strikingly similar. Reference distributions are mildly skewed, but citation distributions with a five-year citation window are highly skewed: the mean is twenty points above the median, while 9-10% of all articles in the upper tail account for about 44% of all citations. The aggregation of sub-fields into disciplines and fields according to several aggregation schemes preserve this feature of citation distributions. It should be noted that when we look into subsets of articles within the lower and upper tails of citation distributions the universality partially breaks down. On the other hand, for 140 of the 219 sub-fields the existence of a power law cannot be rejected. However, contrary to what is generally believed, at the sub-field level the scaling parameter is above 3.5 most of the time, and power laws are relatively small: on average, they represent 2% of all articles and account for 13.5% of all citations. The results of the aggregation into disciplines and fields reveal that power law algebra is a subtle phenomenon.