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Gender Technology and Development
Article . 2024 . Peer-reviewed
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image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
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Exploring stereotypes, bias, and expectations of women in the open data context

Authors: Lorena R. Romero-Domínguez; Andrea Sixto-Costoya; Antonia Ferrer-Sapena;

Exploring stereotypes, bias, and expectations of women in the open data context

Abstract

[EN] This study aims to provide a qualitative analysis of the knowledge, beliefs, and perspectives of prominent women working in different sectors in the context of open data. The analysis is based on semi-structured interviews with an outline developed for the interview script. The content obtained was analyzed by attending to categories that deal directly with a gender perspective, covering existing biases, and possible modes of transformation through actions at individual, collective, and institutional levels that incorporate gender sensitivity when working with open data. The findings show that gender bias persists in the process of collecting, analyzing, and interpreting data, as well as in the formation of work groups. The solution, highlighted by all interviewees, involves the development of interoperable and consistent data collection and analysis models that integrate a gender perspective right from the outset, as well as the training of professionals in fields associated with the use of data. Central to this process is the role of government agencies, which should promote efficient public policies giving greater visibility to the roles of women in areas of knowledge associated with open data. Finally, this research also points to the need for innovations such as artificial intelligence to overcome these challenges.

This publication has been produced with the financial support of the Conselleria de Participacion, Transparencia, Cooperacion y Calidad Democratica. The authors received the ASC postdoctoral grant from the Spanish Ministry of Education and Professional Training (MS21-020).

Keywords

Artificial intelligence, Bias, bancs de dades, Open data, treball, BIBLIOTECONOMIA Y DOCUMENTACION, dones, Gender perspective

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selected citations
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This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
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