Crossing the hurdle: the determinants of individual scientific performance

An original cross sectional dataset referring to a medium sized Italian university is implemented in order to analyze the determinants of scientific research production at individual level. The dataset includes 942 permanent researchers of various scientific sectors for a three year time span (2008 - 2010). Three different indicators - based on the number of publications or citations - are considered as response variables. The corresponding distributions are highly skewed and display an excess of zero - valued observations. In this setting, the goodness of fit of several Poisson mixture regression models are explored by assuming an extensive set of explanatory variables. As to the personal observable characteristics of the researchers, the results emphasize the age effect and the gender productivity gap, as previously documented by existing studies. Analogously, the analysis confirm that productivity is strongly affected by the publication and citation practices adopted in different scientific disciplines. The empirical evidence on the connection between teaching and research activities suggests that no univocal substitution or complementarity thesis can be claimed: a major teaching load does not affect the odds to be a non-active researcher and does not significantly reduce the number of publications for active researchers. In addition, new evidence emerges on the effect of researchers administrative tasks, which seem to be negatively related with researcher's productivity, and on the composition of departments. Researchers' productivity is apparently enhanced by operating in department filled with more administrative and technical staff, and it is not significantly affected by the composition of the department in terms of senior or junior researchers.Comment: Revised version accepted for publication by Scientometric

A theoretical model of the relationship between the h-index and other simple citation indicators

Of the existing theoretical formulas for the h-index, those recently suggested by Burrell (J Informetr 7: 774-783, 2013b) and by Bertoli-Barsotti and Lando (J Informetr 9(4): 762-776, 2015) have proved very effective in estimating the actual value of the h-index Hirsch (Proc Natl Acad Sci USA 102: 16569-16572, 2005), at least at the level of the individual scientist. These approaches lead (or may lead) to two slightly different formulas, being based, respectively, on a "standard'' and a "shifted'' version of the geometric distribution. In this paper, we review the genesis of these two formulas-which we shall call the "basic'' and "improved'' Lambert-W formula for the h-index-and compare their effectiveness with that of a number of instances taken from the well-known Glanzel-Schubert class of models for the h-index (based, instead, on a Paretian model) by means of an empirical study. All the formulas considered in the comparison are "ready-to-use'', i.e., functions of simple citation indicators such as: the total number of publications; the total number of citations; the total number of cited paper; the number of citations of the most cited paper. The empirical study is based on citation data obtained from two different sets of journals belonging to two different scientific fields: more specifically, 231 journals from the area of "Statistics and Mathematical Methods'' and 100 journals from the area of "Economics, Econometrics and Finance'', totaling almost 100,000 and 20,000 publications, respectively. The citation data refer to different publication/citation time windows, different types of "citable'' documents, and alternative approaches to the analysis of the citation process ("prospective'' and "retrospective''). We conclude that, especially in its improved version, the Lambert-W formula for the h-index provides a quite robust and effective ready-to-use rule that should be preferred to other known formulas if one's goal is (simply) to derive a reliable estimate of the h-index.Web of Science11131448141

Growth with competing technologies and bounded rationality

I develop a model of growth based on three assumptions: first, a variety of technologies characterised by different degrees of labour skill intensity, where technological change is localized; second, agents are boundedly rational, and the aggregate rule of motion of their behaviour follows a replicator dynamics; third, markets do not clear instantaneously, with prices adjusting gradually. For simplicity, I study the case of two technologies and two labour markets, one for skilled and one for unskilled labour. The model is investigated by means of local stability and computer numerical analysis. Two types of steady states obtain, each characterised by the complete specialization of production into one of the two technologies. Convergence towards the low-growth steady state, associated with the unskilled labour intensive technology, occurs under adverse structural conditions, such as marked initial skill shortage and high skill upgrade costs. This result of lock-in to the inferior steady state is interpreted as co-ordination failure, in that market forces do not always provide sufficient incentives to ensure a high-growth path

Probing Multivariate Indicators for Academic Evaluation

We combine the Integrated Impact Indicator (I3) and the h-index into the I3-type framework and introduce the publication vector X = (X1, X2, X3) and the citation vector Y = (Y1, Y2, Y3) , the publication score I3X=X1+X2+X3 and the citation score I3Y=Y1+Y2+Y3, and alternative indicators based on percentile classes generated by the h-index. These multivariate indicators can be used for academic evaluation. The empirical studies show that the h-core distribution is suitable to evaluate scholars, the X1 and Y1 are applied to measure core impact power of universities, and I3X and I3Y are alternatives of journal impact factor (JIF). The multivariate indicators provide a multidimensional view of academic evaluation with using the advantages of both the h-index and I3.Comment: Journal of Library Science in China (in press). This is the English version of a Chinese article. DOI:10.13530/j.cnki.jlis.17001

Growth with competing technologies and bounded rationality

I develop a model of growth based on three assumptions: first, a variety of technologies characterised by different degrees of labour skill intensity, where technological change is localized; second, agents are boundedly rational, and the aggregate rule of motion of their behaviour follows a replicator dynamics; third, markets do not clear instantaneously, with prices adjusting gradually. For simplicity, I study the case of two technologies and two labour markets, one for skilled and one for unskilled labour. The model is investigated by means of local stability and computer numerical analysis. Two types of steady states obtain, each characterised by the complete specialization of production into one of the two technologies. Convergence towards the low-growth steady state, associated with the unskilled labour intensive technology, occurs under adverse structural conditions, such as marked initial skill shortage and high skill upgrade costs. This result of lock-in to the inferior steady state is interpreted as co-ordination failure, in that market forces do not always provide sufficient incentives to ensure a high-growth path. <br><br> alternative title: Growth With Competing TechnologiesAnd Bounded Rationality

A Dynamic Network Model to Explain the Development of Excellent Human Performance

Across different domains, from sports to science, some individuals accomplish excellent levels of performance. For over 150 years, researchers have debated the roles of specific nature and nurture components to develop excellence. In this article, we argue that the key to excellence does not reside in specific underlying components, but rather in the ongoing interactions among the components. We propose that excellence emerges out of dynamic networks consisting of idiosyncratic mixtures of interacting components such as genetic endowment, motivation, practice, and coaching. Using computer simulations we demonstrate that the dynamic network model accurately predicts typical properties of excellence reported in the literature, such as the idiosyncratic developmental trajectories leading to excellence and the highly skewed distributions of productivity present in virtually any achievement domain. Based on this novel theoretical perspective on excellent human performance, this article concludes by suggesting policy implications and directions for future research

Diversity, variability and persistence elements for a non-equilibrium theory of eco-evolutionary dynamics

Natural ecosystems persist in variable environments by virtue of a suite of traits that span from the individual to the community, and from the ecological to the evolutionary scenarios. How these internal characteristics operate to allow living beings to cope with the uncertainty present in their environments is the subject matter of quantitative theoretical ecology. Under the framework of structural realism, the present dissertation project has advocated for the strategy of mathematical modeling as a strategy of abstraction. The goal is to explore if a range of natural ecosystems display the features of complex systems, and evaluate whether these features provide insights into how they persist in their current environments, and how might they cope with changing environments in the future. A suite of inverse, linear and non-linear dynamical mathematical models, including non-equilibrium catastrophe models, and structured demographic approaches is applied to five case studies of natural systems fluctuating in the long-term in diverse scenarios: phytoplankton in the global ocean, a mixotrophic plankton food web in a marine coastal environment, a wintering waterfowl community in a major Mediterranean biodiversity hot-spot, a breeding colony of a keystone avian scavenger in a mountainous environment and the shorebird community inhabiting the coast of UK. In all case studies, there is strong evidence that ecosystems are able to closely track their common environment through several strategies. For example, in global phytoplankton communities, a latitudinal gradient in the positive impact of functional diversity on community stability counteracts the increasing environmental variability with latitude. Mixotrophy, by linking several feeding strategies in a food web, internally drives community dynamics to the edge of instability while maximizing network complexity. In contrast, an externally generated major perturbation, operating through planetary climatic disruptions, induce an abrupt regime shift between alternative stable states in the wintering waterfowl community. Overall, the natural systems studied are shown to posses features of complex systems: connectivity, autonomy, emergence, non-equilibrium, non-linearity, self-organization and coevolution. In rapidly changing environments, these features are hypothesized to allow natural system to robustly respond to stress and disturbances to a large extent. At the same time, future scenarios will be probably characterized by conditions never experienced before by the studied systems. How will they respond to them, is an open question. Based on the results of this dissertation, future research directions in theoretical quantitative ecology will likely benefit from non-autonomous dynamical system approaches, where model parameters are a function of time, and from the deeper exploration of global attractors and the non-equilibriumness of dynamical systems