1,720,999 research outputs found
Carenza di cervelli tra fabbisogno e “drenaggio”: le analisi del World Economic Forum
No full textIl secolo appena iniziato presenta, rispetto al passato, una scarsità relativa di talenti che non conosce uguali nella storia dell’umanità e che, rimanendo inalterata in futuro, potrebbe generare un forte rallentamento nel processo di crescita economica dell’intero pianeta. Nessun Paese, nessun organismo – sia pubblico che privato – è destinato a essere competitivo se rimane privo di talenti che, rispetto ad altri tipi di risorse, costituiscono la vera e propria forza motrice del sistema economico mondiale. Per tutte le economie del globo, pertanto, si pone l’urgenza di introdurre politiche che favoriscano lo sviluppo nonché la spendibilità delle competenze e delle abilità di ogni individuo
Misurare il Brain Drain: missione possibile? Rassegna dei principali contributi demo-economici sulla quantificazione e modellizzazione dei flussi migratori qualificati
No full textThe aim of this paper is to provide an overview of the major empirical
relevancies on the brain drain phenomena. The researches
by Carrington and Detragiache (1998), Adams (2003), Dumont and
Lemaitre (2003), Docquier and Rapoport (2005), Defoort (2008) and
Docquier, Lowell and Marfouk (2009) have been particularly useful
to establish a methodology to quantify the brain drain in terms
of outflows from Developing countries and inflows into Developed
countries. However, on a preliminary basis, it may be noted that the
lack of reliable data as well as the absence of a harmonic definition -
at an international level – on “qualified migrants”, makes it difficult
modeling and analyzing this particular type of international mobility
A weighted distance-based approach with boosted decision trees for label ranking
No full textLabel Ranking (LR) is an emerging non-standard supervised classification problem with practical applications in different research fields. The Label Ranking task aims at building preference models that learn to order a finite set of labels based on a set of predictor features. One of the most successful approaches to tackling the LR problem consists of using decision tree ensemble models, such as bagging, random forest, and boosting. However, these approaches, coming from the classical unweighted rank correlation measures, are not sensitive to label importance. Nevertheless, in many settings, failing to predict the ranking position of a highly relevant label should be considered more serious than failing to predict a negligible one. Moreover, an efficient classifier should be able to take into account the similarity between the elements to be ranked. The main contribution of this paper is to formulate, for the first time, a more flexible label ranking ensemble model which encodes the similarity structure and a measure of the individual label importance. Precisely, the proposed method consists of three item-weighted versions of the AdaBoost boosting algorithm for label ranking. The predictive performance of our proposal is investigated both through simulations and applications to three real datasets
The derivative-based approach to nonlinear mediation models: insights and applications
Full text linkTraditional mediation analysis has been developed in the context of linear models, enabling
the estimation of indirect effects through the product of regression coefficients. However,
in the presence of nonlinearities, defining and estimating indirect effects becomes more
challenging. While nonlinear mediation models are relatively easy to address in the
counterfactual-based framework, very few generalizations to nonlinear associational
settings have been proposed. One of the most intuitive is the derivative-based approach
that, however, seems not to be widely spread among scholars. In this paper, we deepen such
an approach to nonlinear mediation models, clarifying and proposing solutions to some
issues which have not been addressed by the previous literature. Specifically, we discussed
discrete exposures, binary mediators and extensions of this approach to more complex
settings like the multilevel one. We also propose to estimate confidence intervals for the
indirect effect within a Bayesian framework and compare its performance to that of other
approaches in the literature through a simulation study. Finally, a real data application is
presented
Improving knowledge, talent and competitiveness: which best practice for the brain drain?
No full textStatistically validated network for analysing textual data
Full text linkThis paper presents a novel methodology, called Word Co-occurrence SVN topic
model (WCSVNtm), for document clustering and topic modeling in textual datasets.
This method represents the corpus as a bipartite network of words and documents
to rigorously assess the statistical significance of word co-occurrences within documents
and document overlap based on shared vocabulary. By employing the Leiden
community detection algorithm to the SVN, distinct communities of words can be
identified and interpreted as topics. Similarly, documents can be sorted into groups
based on their thematic similarities. We demonstrate the effectiveness of our approach
by analyzing three datasets: a set of 120 Wikipedia articles, the arXiv10 dataset, which
consists of 100,000 abstracts from scientific papers, and a sampled subset of 10,000
documents from the original arXiv10. To benchmark our results, we compare our
approach with several well-established models in the field of topic modeling and document
clustering, including the hierarchical Stochastic Block Model (hSBM), BERTopic,
and Latent Dirichlet Allocation (LDA). The results show that WCSVNtm achieves competitive
performance across all datasets, automatically selecting the number of topics
and document clusters, whereas state-of-the-art methods require prior knowledge
or additional tuning for optimization. Finally, any advancements in community detection
algorithms could further improve our method
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
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