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Presenting data on all full-length articles in the three top general economics journals for one year in each decade 1960s—2010s, I analyze changes in patterns of coauthorship, age structure and methodology, and their possible causes. The distribution of number of authors has shifted steadily rightward. In the last two decades, the fraction of older authors has almost quadrupled. Top journals are publishing many fewer papers that represent pure theory, regardless of subfield, somewhat less empirical work based on publicly available data sets, and many more empirical studies based on data collected by the author(s) or on laboratory or field experiments.

This article investigates the extent to which citation and publication patterns differ between men and women in the international relations (IR) literature. Using data from the Teaching, Research, and International Policy project on peer-reviewed publications between 1980 and 2006, we show that women are systematically cited less than men after controlling for a large number of variables including year of publication, venue of publication, substantive focus, theoretical perspective, methodology, tenure status, and institutional affiliation. These results are robust to a variety of modeling choices. We then turn to network analysis to investigate the extent to which the gender of an article's author affects that article's relative centrality in the network of citations between papers in our sample. Articles authored by women are systematically less central than articles authored by men, all else equal. This is likely because (1) women tend to cite themselves less than men, and (2) men (who make up a disproportionate share of IR scholars) tend to cite men more than women. This is the first study in political science to reveal significant gender differences in citation patterns and is especially meaningful because citation counts are increasingly used as a key measure of research's quality and impact.

We estimate the magnitude of spillovers generated by 112 academic “superstars” who died prematurely and unexpectedly, thus providing an exogenous source of variation in the structure of their collaborators' coauthorship networks. Following the death of a superstar, we find that collaborators experience, on average, a lasting 5% to 8% decline in their quality-adjusted publication rates. By exploring interactions of the treatment effect with a variety of star, coauthor, and star/coauthor dyad characteristics, we seek to adjudicate between plausible mechanisms that might explain this finding. Taken together, our results suggest that spillovers are circumscribed in idea space, but less so in physical or social space. In particular, superstar extinction reveals the boundaries of the scientific field to which the star contributes—the “invisible college.”

We have collected and cleaned two network data sets: Coauthorship and Citation networks for statisticians. The data sets are based on all research papers published in four of the top journals in statistics from 2003 to the first half of 2012. We analyze the data sets from many different perspectives, focusing on (a) productivity, patterns and trends, (b) centrality and (c) community structures. For (a), we find that over the 10-year period, both the average number of papers per author and the fraction of self citations have been decreasing, but the proportion of distant citations has been increasing. These findings are consistent with the belief that the statistics community has become increasingly more collaborative, competitive and globalized. For (b), we have identified the most prolific/collaborative/highly cited authors. We have also identified a handful of \"hot\" papers, suggesting \"Variable Selection\" as one of the \"hot\" areas. For (c), we have identified about 15 meaningful communities or research groups, including large-size ones such as \"Spatial Statistics,\" \"Large-Scale Multiple Testing\" and \"Variable Selection\" as well as small-size ones such as \"Dimensional Reduction,\" \"Bayes,\" \"Quantile Regression\" and \"Theoretical Machine Learning.\" Our findings shed light on research habits, trends and topological patterns of statisticians. The data sets provide a fertile ground for future research on social networks.

The concepts of sustainable development have experienced extraordinary success since their advent in the 1980s. They are now an integral part of the agenda of governments and corporations, and their goals have become central to the mission of research laboratories and universities worldwide. However, it remains unclear how far the field has progressed as a scientific discipline, especially given its ambitious agenda of integrating theory, applied science, and policy, making it relevant for development globally and generating a new interdisciplinary synthesis across fields. To address these questions, we assembled a corpus of scholarly publications in the field and analyzed its temporal evolution, geographic distribution, disciplinary composition, and collaboration structure. We show that sustainability science has been growing explosively since the late 1980s when foundational publications in the field increased its pull on new authors and intensified their interactions. The field has an unusual geographic footprint combining contributions and connecting through collaboration cities and nations at very different levels of development. Its decomposition into traditional disciplines reveals its emphasis on the management of human, social, and ecological systems seen primarily from an engineering and policy perspective. Finally, we show that the integration of these perspectives has created a new field only in recent years as judged by the emergence of a giant component of scientific collaboration. These developments demonstrate the existence of a growing scientific field of sustainability science as an unusual, inclusive and ubiquitous scientific practice and bode well for its continued impact and longevity.

It is surprising that the prevailing performance taxonomy for scientists (star versus nonstar) focuses only on individual output and ignores social behavior, because innovation is often characterized as a communal process. To develop a deeper understanding of the mechanisms by which scientists influence the productivity of others, I expand the traditional taxonomy of scientists that focuses solely on productivity and add a second, social dimension: helpfulness to others. Using a combination of academic paper publications and citations to capture scientist productivity and the receipt of academic paper acknowledgments to measure helpfulness, I examine the change in publishing output of the coauthors of 149 scientists that die. Coauthors of highly helpful scientists that die experience a decrease in output quality but not output quantity . Meanwhile, the deaths of high productivity scientists that are not highly helpful do not influence their coauthors' output. In addition, scientists who are helpful with conceptual feedback (critique and advice) have a larger impact on the performance of their coauthors than scientists who provide help with material access, scientific tools, or technical work. Within the context of evaluating scientific productivity, it may be time to update our conceptualization of a \"star.\" This paper was accepted by Lee Fleming, entrepreneurship and innovation.

All authors contributed equally to this work. Besides for MAF, author order was computed randomly. [...]a good leader must know when to delegate tasks and share the workload, e.g., by delegating facilitators for a meeting or assigning responsibilities and subleaders for sections of a manuscript. When team members are not native speakers in the working language, you should always speak slowly, enunciate clearly, and avoid local expressions and acronyms, as well as listen closely and ask questions if you do not understand. Besides language, be empathetic when listening to others’ opinions in order to genuinely understand your coauthors’ points of view [26]. The latter is especially challenging when collaborating over large distances and not meeting face-to-face. Since there is no single “right approach,” our rules can serve as a starting point that can be modified specifically to your own team and project needs.

Small-world networks have attracted much theoretical attention and are widely thought to enhance creativity. Yet empirical studies of their evolution and evidence of their benefits remain scarce. We develop and exploit a novel database on patent coauthorship to investigate the effects of collaboration networks on innovation. Our analysis reveals the existence of regional small-world structures and the emergence and disappearance of giant components in patent collaboration networks. Using statistical models, we test and fail to find evidence that small-world structure (cohesive clusters connected by occasional nonlocal ties) enhances innovative productivity within geographic regions. We do find that both shorter path lengths and larger connected components correlate with increased innovation. We discuss the implications of our findings for future social network research and theory as well as regional innovation policies.

The incidence of extramural collaboration in academic research activities is increasing as a result of various factors. These factors include policy measures aimed at fostering partnership and networking among the various components of the research system, policies which are in turn justified by the idea that knowledge sharing could increase the effectiveness of the system. Over the last two decades, the scientific community has also stepped up activities to assess the actual impact of collaboration intensity on the performance of research systems. This study draws on a number of empirical analyses, with the intention of measuring the effects of extramural collaboration on research performance and, indirectly, verifying the legitimacy of policies that support this type of collaboration. The analysis focuses on the Italian academic research system. The aim of the work is to assess the level of correlation, at institutional level, between scientific productivity and collaboration intensity as a whole, both internationally and with private organizations. This will be carried out using a bibliometric type of approach, which equates collaboration with the co-authorship of scientific publications.

Research on social processes in the production of scientific output suggests that the collective research agenda of a discipline is influenced by its structural features, such as \"invisible colleges\" or \"groups of collaborators\" as well as academic \"stars\" that are embedded in, or connect, these research groups. Based on an encompassing dataset that takes into account multiple publication types including journals and chapters in edited volumes, we analyze the complete co-authorship network of all 1,339 researchers in German political science. Through the use of consensus graph clustering techniques and descriptive centrality measures, we identify the ten largest research clusters, their research topics, and the most central researchers who act as bridges and connect these clusters. We also aggregate the findings at the level of research organizations and consider the inter-university co-authorship network. The findings indicate that German political science is structured by multiple overlapping research clusters with a dominance of the subfields of international relations, comparative politics and political sociology. A small set of well-connected universities takes leading roles in these informal research groups.