Sobre a construção dos fichários para exercícios filosóficos a partir do método da imagem-questão

This article is the result of a collective process, triggered between the months of May and August 2022, in the discipline Teaching practices in Philosophy: teaching programs, in the second quarter of 2022, in the degree course in philosophy at UFABC. After the discipline and the proposal to create a binder for teaching philosophy, we consider the relevance of transforming this process into an article, in order to, first, show the foundations that guided the development of the proposal and, secondly, to share with the interested community the developed binders, its purpose of offering another tool for teaching philosophy and fostering,  continuity of this work that is open.Este artigo é resultado de um processo coletivo, desencadeado entre os meses de maio e agosto de 2022, na disciplina Práticas de ensino de filosofia: programas de ensino, no segundo quadrimestre de 2022, no curso de licenciatura em filosofia da Universidade Federal do ABC (UFABC). Encerrada a disciplina e a proposta de criação de um fichário para ensino de filosofia, consideramos a relevância de transformar esse processo em um artigo, a fim de, em primeiro lugar, mostrar os fundamentos que orientaram o desenvolvimento da proposta e, em segundo lugar, compartilhar com a comunidade interessada os fichários desenvolvidos, seu propósito de oferecer mais uma ferramenta para o ensino de filosofia e fomentar, inclusive, a continuidade deste trabalho que está em aberto

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.Peer reviewe

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical science. © The Author(s) 2019. Published by Oxford University Press