Academic team formation as evolving hypergraphs

This paper quantitatively explores the social and socio-semantic patterns of constitution of academic collaboration teams. To this end, we broadly underline two critical features of social networks of knowledge-based collaboration: first, they essentially consist of group-level interactions which call for team-centered approaches. Formally, this induces the use of hypergraphs and n-adic interactions, rather than traditional dyadic frameworks of interaction such as graphs, binding only pairs of agents. Second, we advocate the joint consideration of structural and semantic features, as collaborations are allegedly constrained by both of them. Considering these provisions, we propose a framework which principally enables us to empirically test a series of hypotheses related to academic team formation patterns. In particular, we exhibit and characterize the influence of an implicit group structure driving recurrent team formation processes. On the whole, innovative production does not appear to be correlated with more original teams, while a polarization appears between groups composed of experts only or non-experts only, altogether corresponding to collectives with a high rate of repeated interactions

Testing bibliometric indicators by their prediction of scientists promotions

We have developed a method to obtain robust quantitative bibliometric indicators for several thousand scientists. This allows us to study the dependence of bibliometric indicators (such as number of publications, number of citations, Hirsch index...) on the age, position, etc. of CNRS scientists. Our data suggests that the normalized h index (h divided by the career length) is not constant for scientists with the same productivity but differents ages. We also compare the predictions of several bibliometric indicators on the promotions of about 600 CNRS researchers. Contrary to previous publications, our study encompasses most disciplines, and shows that no single indicator is the best predictor for all disciplines. Overall, however, the Hirsch index h provides the least bad correlations, followed by the number of papers published. It is important to realize however that even h is able to recover only half of the actual promotions. The number of citations or the mean number of citations per paper are definitely not good predictors of promotion

How does working on university-industry collaborative projects affect science and engineering doctorates' careers? Evidence from a UK research-based university

This paper examines the impact of industrial involvement in doctoral projects on the particular nature of the training and careers of doctorates. We draw on an original survey of job histories of doctorates in physical sciences and engineering from a research-based university in the UK. Using multivariate probit analysis and linearised (robust) and resampling (jackknife) variance estimation techniques, we found that projects with industrial involvement are associated with higher degree of socialisation with industry. There is some evidence showing that these projects are also more likely to focus on solving firm-specific technical problems or developing firm-specific specifications/prototypes, rather than exploring high-risk concepts or generating knowledge in the subject areas. Crucially, these projects result in fewer journal publications. Not surprisingly, in line with existing literature, we found that engaging in projects with industrial involvement (in contrast to projects without industrial involvement) confers advantages on careers in the private sector. Nevertheless, there is also a hint that engaging in projects with industrial involvement may have a negative effect on careers in academia or public research organisations. While acknowledging that the modelling results are based on a small sample from a research-based university and that therefore the results need to be treated with caution, we address implications for doctorates, universities and policymakers

The dynamics of university units as a multi-level process. Credibility cycles and resource dependencies

This paper presents an analysis of resource acquisition and profile development of institutional units within universities. We conceptualize resource acquisition as a two level nested process, where units compete for external resources based on their credibility, but at the same time are granted faculty positions from the larger units (department) to which they belong. Our model implies that the growth of university units is constrained by the decisions of their parent department on the allocation of professorial positions, which represent the critical resource for most units’ activities. In our field of study this allocation is largely based on educational activities, and therefore, units with high scientific credibility are not necessarily able to grow, despite an increasing reliance on external funds. Our paper therefore sheds light on the implications that the dual funding system of European universities has for the development of units, while taking into account the interaction between institutional funding and third-party funding