A machine learning taxonomic classifier for science publications

Dissertação de mestrado integrado em Engineering and Management of Information SystemsThe evolution in scientific production, associated with the growing interdomain collaboration of knowledge and the increasing co-authorship of scientific works remains supported by processes of manual, highly subjective classification, subject to misinterpretation. The very taxonomy on which this same classification process is based is not consensual, with governmental organizations resorting to taxonomies that do not keep up with changes in scientific areas, and indexers / repositories that seek to keep up with those changes. We find a reality distinct from what is expected and that the domains where scientific work is recorded can easily be misrepresentative of the work itself. The taxonomy applied today by governmental bodies, such as the one that regulates scientific production in Portugal, is not enough, is limiting, and promotes classification in areas close to the desired, therefore with great potential for error. An automatic classification process based on machine learning algorithms presents itself as a possible solution to the subjectivity problem in classification, and while it does not solve the issue of taxonomy mismatch this work shows this possibility with proved results. In this work, we propose a classification taxonomy, as well as we develop a process based on machine learning algorithms to solve the classification problem. We also present a set of directions for future work for an increasingly representative classification of evolution in science, which is not intended as airtight, but flexible and perhaps increasingly based on phenomena and not just disciplines.A evolução na produção de ciência, associada à crescente colaboração interdomínios do conhecimento e à também crescente coautoria de trabalhos permanece suportada por processos de classificação manual, subjetiva e sujeita a interpretações erradas. A própria taxonomia na qual assenta esse mesmo processo de classificação não é consensual, com organismos estatais a recorrerem a taxonomias que não acompanham as alterações nas áreas científicas, e indexadores/repositórios que procuram acompanhar essas mesmas alterações. Verificamos uma realidade distinta do espectável e que os domínios onde são registados os trabalhos científicos podem facilmente estar desenquadrados. A taxonomia hoje aplicada pelos organismos governamentais, como o caso do organismo que regulamenta a produção científica em Portugal, não é suficiente, é limitadora, e promove a classificação em domínios aproximados do desejado, logo com grande potencial para erro. Um processo de classificação automática com base em algoritmos de machine learning apresenta-se como uma possível solução para o problema da subjetividade na classificação, e embora não resolva a questão do desenquadramento da taxonomia utilizada, é apresentada neste trabalho como uma possibilidade comprovada. Neste trabalho propomos uma taxonomia de classificação, bem como nós desenvolvemos um processo baseado em machine learning algoritmos para resolver o problema de classificação. Apresentamos ainda um conjunto de direções para trabalhos futuros para uma classificação cada vez mais representativa da evolução nas ciências, que não pretende ser hermética, mas flexível e talvez cada vez mais baseada em fenómenos e não apenas em disciplinas

Grasping the Links in the Chain: Understanding the Unintended Consequences of International Counter-Narcotics Measures for the EU

No abstract available

Gender mainstreaming research funding: a study of effects on STEM research proposals

Policymakers increasingly try to steer researchers to choose topics of societal concern and to conduct research in ways that reflect such concerns. One increasingly common approach is prompting researchers to integrate certain perspectives into the content of their research, but little is known about the effects of this governance modality. We analyze 1,189 science, technology, engineering, and mathematics research proposals submitted to the Swedish Research Council which, starting in 2020, required all applicants to consider including the sex and/or gender perspectives in their research. We identify three overarching strategies upon which researchers rely (content-, performer-, and impact-centered) and analyze the ways in which researchers across disciplines motivate, through text, the inclusion or exclusion of these perspectives. Based on our findings, we discuss the scope of the desired effect(s) of a requirement of this kind

Assessment tools for disposable and long durability products

The current market situation is characterized by planned obsolescence. It warns the need to design in a more efficiently way, by optimizing the recycle and disassembly operations and lowering the impact on the environment of all kind of products, from the easiest to the most complex ones. This paper focuses on short-lived and long durability products by analyzing them respectively according to the methodologies developed by the Observatory of EcoPack (OEP) and the Design by Components (DC) that share the same general framework and scenario. For disposable products, i.e. packaging, the analysis was carried out with a comparative analysis on components and communication, up to the definition of guidelines for a specific productive sector. Regarding the long durability goods, i.e. household appliances, the analysis is done according to the DC, in which the complex products are simplify to a function-essential structure. This is the starting point for a new design of complex goods focused on disassembly and maintenance. These two methodologies are able to provide useful tools for designing and innovating, through a scientific quali-quantitative analysis on products that are currently on the market