1,720,966 research outputs found
Competitive project funding and dynamic complex networks: evidence from Projects of National Interest (PRIN)
No full textThis paper aims to study the collaboration among researchers in a specific Italian program funding, the Projects of National Interest (PRIN), which supports the academic research. The paper uses two approaches to study the dynamic complex networks: first it identifies the observed distribution of links among researchers in the four areas of interest (chemistry, physics, economics and sociology) through distribution models, then it uses a stochastic model to understand how the links change over time. The analysis is based on large and unique dataset on 4322 researchers from 98 universities and research institutes that have been selected for PRIN allocation from 2000 to 2011. The originality of this work is that we have studied a competitive funding schemes through dynamic network analysis techniques
Link prediction and feature relevance in knowledge networks: A machine learning approach
No full textWe propose a supervised machine learning approach to predict partnership formation between universities. We focus on successful joint R&D projects funded by the Horizon 2020 programme in three research domains: Social Sciences and Humanities, Physical and Engineering Sciences, and Life Sciences. We perform two related analyses: link formation prediction, and feature importance detection. In predicting link formation, we consider two settings: one including all features, both exogenous (pertaining to the node) and endogenous (pertaining to the network); and one including only exogenous features (thus removing the network attributes of the nodes). Using out-of-sample cross-validated accuracy, we obtain 91% prediction accuracy when both types of attributes are used, and around 67% when using only the exogenous ones. This proves that partnership predictive power is on average 24% larger for universities already incumbent in the programme than for newcomers (for which network attributes are clearly unknown). As for feature importance, by computing super-learner average partial effects and elasticities, we find that the endogenous attributes are the most relevant in affecting the probability to generate a link, and observe a largely negative elasticity of the link probability to feature changes, fairly uniform across attributes and domains
Evaluation for the allocation of university research project funding: Can rules improve the peer review?
No full textEvaluation for the allocation of project-funding schemes devoted to sustain academic research often undergoes changes of the rules for the ex-ante selection, which are supposed to improve the capability of peer review to select the best proposals. How modifications of the rules realize a more accountable evaluation result? Do the changes suggest an improved alignment with the program's intended objectives? The article addresses these questions investigating Research Project of National Interest, an Italian collaborative project-funding scheme for academic curiosity-driven research through a case study design that provides a description of how the changes of the ex-ante evaluation process were implemented in practice. The results show that when government tries to steer the peer-review process by imposing an increasing number of rules to structure the debate among peers and make it more accountable, the peer-review practices remain largely impervious to the change
Datanet: A Stata routine for organising a dataset for network analysis purposes
No full textThis paper presents and applies a new user-written Stata program, datanet, which facilitates the dataset organisation for network analysis purposes. Given a fixed number of units (or nodes) belonging to the same group (there will be a variable denoting group membership), possibly connected one each other or possibly not, this routine creates a new dataset containing all their possible couplings to then be easily exploited using Stata network analysis commands. So far, to our knowledge, no routine has been developed in Stata which executes this type of procedure
Machine learning prediction of academic collaboration networks
No full textWe investigate the different roles played by nodes’ network and non-network attributes in explaining the formation of European university collaborations from 2011 to 2016, in three European Research Council (ERC) domains: Social Sciences and Humanities (SSH), Physical and Engineering Sciences (PE), Life Sciences (LS), as well as multidisciplinary collaborations. On link formation in collaboration networks, existing research has not yet compared and simultaneously examined both network and non-network attributes. Using four machine learning predictive algorithms (LASSO, Neural Network, Gradient Boosting, and Random Forest) our results show that, over various model specifications: (i) best model link formation accuracy is larger than 80%, (ii) among the non-network attributes, public funding plays an important role in PE and LS, (iii) network attributes count more than non-network attributes for the formation, sensibly increasing accuracy, (iv) feature-importance scores show a different ordering in the four domains, thus signalling different modes of knowledge production and transmission taking place within these different scientific communities
The innovation networks of city‐regions in Europe: exclusive clubs or inclusive hubs?
No full textWhich role do city-regions play in European innovation network formation? We study the evolution of innovation collaborative networks in European city-regions outlining two opposite models: in the exclusive network model city-regions establish a closed network of innovators among themselves; in the inclusive network models city-regions build a network of innovators which includes the peripheral regions. Employing a temporal exponential random graph model on 248 regions for the period 2000–2016, we find that the two models coexist. We conclude that in the EU the city-regions act as both engines of generation of innovation and integrators of innovation actors
Organizational factors affecting higher education collaboration networks: evidence from Europe
No full textWe explore the role of organizational factors in research collaboration networks among European universities. The study of organizational drivers in shaping collaboration patterns is crucial for policy design aimed at reducing research fragmentation and fostering knowledge creation and diffusion. By using Exponential Random Graph Models (ERGMs) and controlling for spatial factors, we investigate the role of two main mechanisms guiding the partners’ selection process: organizational attributes and homophily. We investigate two distinct scientific collaboration networks (i.e., projects and publications) and two research domains (Physical Sciences and Engineering, and Life Sciences) over the 2011–2016 time period. Our empirical evidence reveals that, among the main dimensions indicated by the literature, research capability (measured by the dimension of doctoral programs) has the clearest and most stable impact either on the tendency to establish collaboration ties or as homophily effect. In terms of policy implications, it emerges that organizational similarity in research capability matters and policy makers should consider doctoral programs as a strategic variable to promote successful collaborations in scientific research
Moving, remaining, and returning: international mobility of doctorate holders in the social sciences and humanities
No full textInternational mobility of doctorate holders is one important dimension of the general phenomenon of internationalization and globalization of research systems, which is supposed to have positive effects on economy and society. Although issues of international mobility for doctorate holders have been largely investigated, there is still relatively little information about the factors affecting those with degrees specifically in the social sciences and humanities (SSH). Considering this, the aim of the current paper is twofold: first, to investigate whether the propensity of a doctorate holder in SSH to experience a period of international mobility during the career is influenced by mobility during the educational stage; second, to examine whether the mobility after doctoral degree affects the choice of doctorates to return to their country of origin, as opposed to remaining abroad, controlling for peculiar aspects of the higher education sector of employment. The results show that international mobility during graduate education and at the moment of choosing the first job on attaining the PhD are important factors influencing the future mobility of doctorate holders in the SSH areas. These same factors also influence the individual’s propensity to diverge (continue abroad) or converge (remain, return) with respect to their initial country of employment. The results of this investigation improve our knowledge about the effects generated by the early choices of individuals, which could support decision-makers in designing instruments addressing the international mobility of doctorate holders and, when relevant, creating the conditions for their return
Currency Unions and Global Value Chains: The Impact of the Euro on the Italian Value Added Exports
Full text linkMany estimates of the effect of the common currency on trade have been made, although a clear answer has yet to be given. This work analyses the trade effect of the euro by providing a twofold contribution. First, one of the main stylised facts that has emerged from the recent literature is that trade flows in gross terms can differ substantially from those measured in value added terms. Accordingly, we focus on the structure of global value chains rather than conventional gross trade. To this aim, we provide an estimate of the value added trade flows that would have existed between Italy and its main trading partners if Italy had not joined the monetary union and show how, and to what extent, international production sharing has been affected. Second, we use a methodology that is different from traditional, parametric ones. Specifically, we apply the synthetic control method to construct appropriate counterfactuals and estimate the causal impact of the euro. Our empirical analysis provides a relevant case for considering value added in addition to gross trade since it shows that the euro facilitated the forward integration of Italian exports, whereas it slowed down backward integration. Overall, these results suggest that the euro had an impact on Italian global value chain participation by altering value added flows across member as well as non-member states, with great heterogeneity in the results across value added trade components and sectors
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