Predicting the results of evaluation procedures of academics

Background. The 2010 reform of the Italian university system introduced the National Scientific Habilitation (ASN) as a requirement for applying to permanent professor positions. Since the CVs of the 59,149 candidates and the results of their assessments have been made publicly available, the ASN constitutes an opportunity to perform analyses about a nation-wide evaluation process. Objective. The main goals of this paper are: (i) predicting the ASN results using the information contained in the candidates’ CVs; (ii) identifying a small set of quantitative indicators that can be used to perform accurate predictions. Approach. Semantic technologies are used to extract, systematize and enrich the information contained in the applicants’ CVs, and machine learning methods are used to predict the ASN results and to identify a subset of relevant predictors. Results. For predicting the success in the role of associate professor, our best models using all and the top 15 predictors make accurate predictions (F-measure values higher than 0.6) in 88% and 88.6% of the cases, respectively. Similar results have been achieved for the role of full professor. Evaluation. The proposed approach outperforms the other models developed to predict the results of researchers’ evaluation procedures. Conclusions. Such results allow the development of an automated system for supporting both candidates and committees in the future ASN sessions and other scholars’ evaluation procedures

Understanding Collaboration Requirements for Modular Construction and their Cascading Failure Impact on Project Performance

Effective Implementation of Modularization Demands Close Collaboration among the Various Project Stakeholders Due to the Distinct and Complex Needs of Such Construction Method. in Fact, Lack of Adequate Collaboration is One of the Main Factors Impacting Modular Construction Performance. Despite that, No Previous Study Has Yet Addressed Collaboration Requirements in Modular Construction and their Cascading Failure Impact on Project Performance. This Paper Fills Such a knowledge Gap. to This End, the Authors Followed a Multistep Research Methodology. First, Systematic Literature Analysis Was Performed to Identify the Factors Impacting Collaboration and the Impacted Modular Risks as Well as their Cause-Effect Relationships. Second, Two Surveys Were Distributed to Collect (1) Importance Weights and Failure Probabilities for the Collaboration Factors; and (2) Failure Probabilities and Performance Impacts for the Modular Risks. Third, Network Analysis Was Conducted using In- and Out-Degree Centralities to Determine the Most Influential and Sensitive Aspects in Terms of Collaboration. Fourth, Independent Cascade Modeling Was Performed to Capture the Cascading Failure Effect of Various Collaboration Aspects on Project Performance. Ultimately, a Total of 25 Factors Were Found to Impact Collaboration Categorized under Four Themes, Including (1) Project Organization and Control, (2) Stakeholders\u27 Relationships and Characteristics, (3) Information Sharing, Documentation, and Technologies, and (4) Design and Construction Planning. Furthermore, 10 Modular Operation Risks Were Found to Be Impacted by Collaboration in Construction Projects. Although the Most Influential Factors Were Related to Information Sharing, Documentation, and Technologies, the Most Sensitive Factors Fell within the Design and Construction Planning. Most Importantly, Results Show that Inadequate Collaboration during Design and Construction Planning Can Lead to 70.6% Direct Growth in Schedule and Cost of Modularized Projects. This Paper Contributes to the Body of Knowledge by Offering an Unprecedented Framework that Investigates Collaboration Requirements in Modular Construction and their Interdependencies

Analyzing the Impact of Companies on AI Research Based on Publications

Artificial Intelligence (AI) is one of the most momentous technologies of our time. Thus, it is of major importance to know which stakeholders influence AI research. Besides researchers at universities and colleges, researchers in companies have hardly been considered in this context. In this article, we consider how the influence of companies on AI research can be made measurable on the basis of scientific publishing activities. We compare academic- and company-authored AI publications published in the last decade and use scientometric data from multiple scholarly databases to look for differences across these groups and to disclose the top contributing organizations. While the vast majority of publications is still produced by academia, we find that the citation count an individual publication receives is significantly higher when it is (co-)authored by a company. Furthermore, using a variety of altmetric indicators, we notice that publications with company participation receive considerably more attention online. Finally, we place our analysis results in a broader context and present targeted recommendations to safeguard a harmonious balance between academia and industry in the realm of AI research.Comment: Published in Scientometric

Journal Productivity in Fishery Science an informetric analysis

Knowledge is a human resource which has the ability to consolidate the valuable results of human thinking and civilization through different times. It is the totality of understanding of nature and its features for improved quality of life of human society. Because of this, knowledge has been increasing in volume, dimension and directions. The term ‘information’ and 'knowledge' are often used as if they are interchangeable. Information is ‘potential knowledge‘ which is converted into knowledge by the integration of memory of human beings. In modern times there is a confusion on knowledge usage. Therefore an understanding of the concept ‘knowledge’ is needed for formulation of strategies in information science

Promoting transferability: Insights from economic evaluations of public health interventions to tackle malaria in central Senegal

Health economic evaluation seeks to guide priority setting by generating evidence on the relative efficiency of alternative policy choices. Yet, the volume and quality of economic evaluations are insufficient to inform the vast array of policy choices, especially in low- and lower-middle-income countries. This thesis aims to inform policy choices regarding strategies to tackle malaria and to improve methods to transfer economic evaluation evidence across contexts. A bibliometric analysis of the applied economic evaluation literature frames the thesis. Two economic evaluations were conducted sequentially alongside two cluster-randomized controlled trials in approximately the same population of over 500,000 people in four districts of central Senegal. The first evaluation explored the financial and economic costs of equipping community health workers to deliver seasonal malaria chemoprevention (SMC) door-to-door to children under 10 years of age. It revealed substantial economies of scale, with the largest primary healthcare facility catchment areas (by population) incurring the lowest average costs per child treated. The second evaluation assessed the costs and cost-effectiveness of several multi-component, geographically targeted, malaria strategies in a low transmission context. Building on the analysis of SMC, the data collected in the second trial was used to develop and populate a simple, transparent, flexible, and intuitive cost model, which projects how the costs of four interventions may be expected to vary outside the study setting, in other contexts, and with certain changes to the interventions themselves, as well as with input prices and epidemiology. Drawing on the two economic evaluations and a critical review of wide-ranging literatures relevant to transferability, the thesis concludes by proposing guidance for the design and conduct of economic evaluations alongside trials or pilots in ways that promote transferability. In particular, it recommends efforts from the outset of the evaluation to identify and narrow the “transferability gap” between planned implementation within the trial or pilot and the intended decision contexts

Análise da produção científica sobre o papel do escritório de projetos na gestão do conhecimento período de 2004 a 2014

Orientador : Prof. Dr. José Simão de Paula PintoDissertação (mestrado) - Universidade Federal do Paraná, Setor de Ciências Sociais Aplicadas, Programa de Pós-Graduação em Ciência, Gestão e Tecnologia da Informação. Defesa: Curitiba, 27/02/2015Inclui referênciasResumo: Analisa a produção científica referente ao período de 2004 a 2014, de artigos publicados em periódicos científicos indexados por bases indexadoras, sobre gestão do conhecimento e gestão de projetos, programas e portfólio (Grupo I) e sobre gestão do conhecimento em projetos, programas e portfólio e o escritório de gestão de projetos, programas e portfólio (Grupo II). Foi utilizado método informétrico baseado na lei de Zipf (frequência das palavras), Luhn (posicionamento das palavras de maior conteúdo semântico) e Goffman (determinação do ponto de transição) para identificar os termos que melhor descrevem o conteúdo dos dois grupos de artigos. Para caracterizar a produção científica, foram utilizados métodos bibliométricos baseado nas leis de Lotka (produtividade de autores) e Bradford (produtividade de periódicos), e cientométrico (países e instituições com maior produção e citações), com a utilização do software HistCite para as análises bibliométricas. O experimento informétrico baseado na lei de Zipf para o Grupo I identificou 237 palavras-chave as quais apresentaram 79% de compatibilidade quando comparadas às palavras-chave fornecidas pelos autores. O experimento informétrico baseado na lei de Zipf para o Grupo II identificou 8 palavras-chave as quais apresentaram 63% de compatibilidade comparadas às palavras chave fornecidas pelos autores. Para o Grupo I foram identificados 41 autores com 3 ou mais contribuições no período e para o Grupo II foi identificado 1 autor com 3 contribuições no período. Palavras-chave: Informetria. Bibliometria. Cientometria. Gestão do conhecimento. Gestão de projetos. Gestão de programas de projetos. Gestão de portfólio de projetos. Escritório de gerenciamento de projetos.Abstract: This research analyzes the scientific production of papers published in scientific journals from 2004 to 2014 and indexed by , on knowledge management and project, program and portfolio management, (referred as Group I) and on knowledge management, project, program and portfolio management, and project management office (referred as Group II). The informetric method used was based on Zipf's law (words frequency), Luhn (position of the words with higher semantic content) and Goffman (transition point) to identify terms that better describe the contents of the two groups of articles. Bibliometric methods were used in order to characterize the scientific production, were used bibliometric methods based on the laws of Lotka (author productivity) and Bradford (journal's productivity), and scientometric (countries and institutions with higher production and quotes), using the HistCite software. The Group I papers informetric experiment, based on Zipf's law, has identified 237 keywords which showed 79% compatibility compared to the keywords provided by the authors. The Group II informetric experiment, based on Zipf's law, identified 8 keywords which showed 63% compatibility compared to the keywords provided by the authors. For Group I there are 41 authors identified with 3 or more contributions in the period and the Group II was identified one author with three contributions in the period. Key-words: Informetrics. Bibliometrics. Scientometrics. Knowledge management. Project management. Project program management. Project portfolio management. Project management office