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

Digital Tools for Innovative Higher Education Teaching - A Scoping Review of Empirical Studies

Since the COVID-19 pandemic has outbursts, changes in the teaching process are observable. What was a temporary countermeasure against the pandemic is now considered a didactic tool. More and more teachers and entire higher education institutions decided to permanently implement digital tools or innovative teaching methods into the didactic process. The continuous development of technology fosters innovation in the teaching process. It allows teachers to use newer and newer teaching tools, better and better adapted to the real needs of students. The main goal of this article is to point out, by a scoping review of the papers published between 2020 and 2023, the digital tools used in the teaching process at the higher education level. The review focuses only on the original articles written in English, which present studies on implementing innovative digital teaching tools. The article is a form of a preliminary catalog of didactic tools used at the higher education level in the last three years, with their quantitative presentation. The tools have been categorized according to the technologies they use and then assigned to the scientific disciplines in accordance with the OECD classification in which they were used

Academic staff quality in higher education : an empirical analysis of Portuguese public administration education

Higher education accreditation frameworks typically consider academic staff quality a key element. This article embarks on an empirical study of what academic staff quality means, how it is measured, and how different aspects of staff quality relate to each other. It draws on the relatively nascent Portuguese experience with study programme accreditation. The study provides an analysis of staff quality in public administration education, an area of massive expansion in recent years. Several dimensions of quality are assessed (staff qualifications, research intensity, disciplinary orientation, diversity, international orientation, professional orientation, and inbreeding) along with the interactions that occur between them. A statistical analysis is made of the indicators for all 21 study programmes in the area of public administration, involving 236 academics in 6 public universities. We find that, in general, the quality of academic staff complies with standards, but there are issues regarding qualifications and research intensity that need to be addressed. The findings emphasize the need to uphold academic staff quality standards but calls for policies to curtail possible gaming resulting from it. The article illustrates the relevance of analysing staff quality from an empirical point of view and its contribution to our understanding of how different quality accreditation processes function and their implications for how quality is achieved in higher education.info:eu-repo/semantics/publishedVersio

Do articles in open access journals have more frequent altmetric activity than articles in subscription-based journals? An investigation of the research output of Finnish universities

Scientific articles available in Open Access (OA) have been found to attract more citations and online attention to the extent that it has become common to speak about OA Altmetrics Advantage. This research investigates how the OA Altmetrics Advantage holds for a specific case of research articles, namely the research outputs from universities in Finland. Furthermore, this research examines disciplinary and platform specific differences in that (dis)advantage. The new methodological approaches developed in this research focus on relative visibility, i.e. how often articles in OA journals receive at least one mention on the investigated online platforms, and relative receptivity, i.e. how frequently articles in OA journals gain mentions in comparison to articles in subscription-based journals. The results show significant disciplinary and platform specific differences in the OA advantage, with articles in OA journals within for instance veterinary sciences, social and economic geography and psychology receiving more citations and attention on social media platforms, while the opposite was found for articles in OA journals within medicine and health sciences. The results strongly support field- and platform-specific considerations when assessing the influence of journal OA status on altmetrics. The new methodological approaches used in this research will serve future comparative research into OA advantage of scientific articles over time and between countries.</p

FWF Clusters of Excellence. Evaluation of the selection process for the first call for proposals

In 2021, the Austrian Science Fund (FWF) launched its Clusters of Excellence (CoE) programme as the first stage in the excellent=austria initiative. The plan was to fund about four clusters – consisting of three to eight collaborating organisations each – selected based on a 2-stage international peer review process. The clusters’ annual budgets can be €2 to 7 million, of which the FWF funds 60% and the research institutions hosting the clusters the remaining 40%. FWF provides funding for five years, with the possibility of an additional five years. This report presents the results of an accompanying evaluation of the selection procedures for the first call for Clusters of Excellence, and recommendations for developing the procedures for the second call. It is based on analyses of application and review data, surveys to applicants and expert reviewers, interviews with host institutions, members of the international jury assessing the proposals, the FWF board and other stakeholders, as well as observation of jury meetings. A statement by the Austrian Science Fund (FWF) on this evaluation can be found at: https://doi.org/10.5281/zenodo.836328

Indicadores y estadísticas de investigación UGR 2020. Lite version

1. La UGR registra su mayor crecimiento en el número de publicaciones científicas de la última década. Desde 2010-2011, cuando se produjo el último gran incremento de publicaciones Web of Science, la UGR ha ido creciendo a un ritmo sostenido pero moderado. En el Gráfico 1 se muestra el total de publicaciones en Web of Science Core Collection que alcanza la cifra de 4349. Si atendemos solo a las publicaciones citables (artículo, revisiones y cartas) en revistas de prestigio la cifra de 2019 es de 3665. Este dato supone un incremento de un 7% en relación al año anterior, hemos de considerar que entre 2011-2016 el crecimiento era inferior 4% y que en los años 2017 y 2018 apenas se registro incremento alguno. 2. En relación a los indicadores de impacto, seguimos manteniendo un nivel de publicación elevado en revistas de prestigio. Aumentamos el número y porcentaje de publicaciones indexadas en revistas del Primer Cuartil hasta alcanzar el 54%. En relación al número de publicaciones Q1 se ha logrado pasar de las 1437 de 2018 a las 1706 de 2019. Este registro supone un nuevo hito ya que es el mayor incremento de la década, tanto si consideramos datos brutos como porcentuales. 3. El número de publicaciones y su impacto no son los únicos que presentan registros positivos, también el número de colaboraciones con centros e instituciones internacionales en publicaciones de prestigio alcanza su mejor cifra. La tasa de colaboración internacional se sitúa en el 56%, la mejor de la década e implica que más de la mitad de los trabajos que publicamos se realizan en un contexto de colaboración internacional. El número de publicaciones con colaboraciones internacional se sitúa en 1844. 4. Como novedad en la memoria de este año hemos incluido un nuevo indicador: las publicaciones firmadas con liderazgo. En este caso reflejamos el liderazgo científico a través de dos indicadores: el número o porcentaje de publicaciones que firmamos como primer o último o autor de la correspondencia (Número Documentos Liderados) o bien simplemente como el Número o Porcentaje de Documentos Firmados en Primer Lugar. En los últimos diez años los documentos liderados se han situado en el 72% y los firmados en primer lugar en el 53%, en los últimos cinco años esta última tasa registra la misma cifra. Si comparamos estos resultados con las universidades más importantes a nivel nacional el dato es más que positivo ya que la UGR registra una de las mejores tasas de liderazgo en la firma de publicaciones científicas a nivel nacional 5. Junto a los indicadores generales ofrecemos en el informe datos relacionados con las disciplinas y especialidades así como una tabla comparativa con 50 universidades españoles. En relación a esta última la UGR sigue manteniéndose como una las universidades más importantes a nivel nacional y la mejor del sistema universitario andaluz. Finalmente se complementan los indicadores bibliométricos con los resultados preliminares en la obtención de proyectos en las convocatorias Retos y Generación del Conocimiento. Se han conseguido un total de 99 proyectos, cifra superior a la de 2018 y muy similar a la de años anteriores. Pero quizás el dato más positivo es que logramos una financiación de 8.779.699 €, la más alta lograda en este programa. En el contexto nacional somos la 4º universidad que más proyectos de investigación consiga y a nivel autonómico seguimos siendo la primera