Applied sciences, 2022-06, Vol.12 (12), p.5813
2022
Details
- Autor(en) / Beteiligte
- Titel
- Intersectional Study of the Gender Gap in STEM through the Identification of Missing Datasets about Women: A Multisided Problem
- Ist Teil von
- Applied sciences, 2022-06, Vol.12 (12), p.5813
- Ort / Verlag
- Basel: MDPI AG
- Erscheinungsjahr
- 2022
- Link zum Volltext
- Quelle
- EZB Free E-Journals
- Beschreibungen/Notizen
- This paper discusses the problem of missing datasets for analysing and exhibiting the role of women in STEM with a particular focus on computer science (CS), artificial intelligence (AI) and data science (DS). It discusses the problem in a concrete case of a global south country (i.e., Mexico). Our study aims to point out missing datasets to identify invisible information regarding women and the implications when studying the gender gap in different STEM disciplines. Missing datasets about women in STEM show that the first step to understanding gender imbalance in STEM is building women’s history by “completing” existing datasets.
- Sprache
- Englisch
- Identifikatoren
- ISSN: 2076-3417eISSN: 2076-3417DOI: 10.3390/app12125813Titel-ID: cdi_doaj_primary_oai_doaj_org_article_e58c1c850c454ed2a60a42a9958d5249
- Format
- –
- Schlagworte
- Artificial intelligence, Computer Science, data, Data collection, Data science, data-driven studies, Datasets, Economic summit conferences, Employment, Engineering, Equality, Experiments, Gender, Gender aspects, gender gap, Intersectionality, missing datasets, Occupations, women in artificial intelligence, women in data science, Workforce