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  • × author_ss:"Chaves Guimarães, J.A."
  • × language_ss:"e"
  • × year_i:[2020 TO 2030}
  1. Sales, R. de; Martínez-Ávila, D.; Chaves Guimarães, J.A.: James Duff Brown : a librarian committed to the public library and the subject classification (2021) 0.02 
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    Abstract
    After two decades in the 21st Century, and despite all the advances in the area, some very important names from past centuries still do not have the recognition they deserve in the global history of library and information science and, specifically, of knowledge organization. Although acknowledged in British librarianship, the name of James Duff Brown (1862-1914) still does not have a proper recognition on a global scale. His contributions to a free and more democratic library had a prominent place in the works and projects he developed during his time at the libraries of Clerkenwell and Islington in London. Free access to the library shelves, an architecture centered on books and people, and classifications that are more dynamic were dreams fulfilled by Brown. With this biographical article, we hope to live up to his legacy and pay homage to a true librarian and an advocate of the public library and subject classification.
  2. Bagatini, J.A.; Chaves Guimarães, J.A.: Algorithmic discriminations and their ethical impacts on knowledge organization : a thematic domain-analysis (2023) 0.00 
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    Abstract
    Personal data play a fundamental role in contemporary socioeconomic dynamics, with one of its primary aspects being the potential to facilitate discriminatory situations. This situation impacts the knowledge organization field especially because it considers personal data as elements (facets) to categorize persons under an economic and sometimes discriminatory perspective. The research corpus was collected at Scopus and Web of Science until the end of 2021, under the terms "data discrimination", "algorithmic bias", "algorithmic discrimination" and "fair algorithms". The obtained results allowed to infer that the analyzed knowledge domain predominantly incorporates personal data, whether in its behavioral dimension or in the scope of the so-called sensitive data. These data are susceptible to the action of algorithms of different orders, such as relevance, filtering, predictive, social ranking, content recommendation and random classification. Such algorithms can have discriminatory biases in their programming related to gender, sexual orientation, race, nationality, religion, age, social class, socioeconomic profile, physical appearance, and political positioning.

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