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Sofia Kypraiou

Publications and source records attributed to Sofia Kypraiou.

2 recordsLinked to original sources

Mapping Data Labour Supply Chain in Africa in an Era of Digital Apartheid: a Struggle for Recognition

Content moderation and data annotation work has shifted to the Global South, particularly Africa, where workers at business process outsourcing (BPO) companies operate under precarity to serve Global North needs. We address the invisibility of this data labour supply chain and the underdocumented working conditions of its workforce. Drawing on a participatory collaboration between academics, an NGO, and a union, we conducted desk research and deployed a questionnaire (n=81) attuned to unions' organising goals. Our findings show that data labour spans 43 out of 55 African countries, involving 17 major firms serving predominantly North-American and European clients, with workers employed on short-term contracts, under psychological stress and economic instability - conditions that obscure the competences, i.e. adaptability and resilience, that their work demands. We contribute the first comprehensive map of Africa's data labour industry and demonstrate a methodology that centers workers' collective actions in documenting their conditions, drawing on Honneth's "struggle for recognition" to capture workers' demands for professional and social acknowledgement.

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Wikigender: A Machine Learning Model to Detect Gender Bias in Wikipedia

The way Wikipedia's contributors think can influence how they describe individuals resulting in a bias based on gender. We use a machine learning model to prove that there is a difference in how women and men are portrayed on Wikipedia. Additionally, we use the results of the model to obtain which words create bias in the overview of the biographies of the English Wikipedia. Using only adjectives as input to the model, we show that the adjectives used to portray women have a higher subjectivity than the ones used to describe men. Extracting topics from the overview using nouns and adjectives as input to the model, we obtain that women are related to family while men are related to business and sports.

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