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Article
Publication date: 1 October 2000

Walter Leal Filho, Katarina Larsen and Folke Snickars

This paper presents the main results of a research project looking at trends on environmental technology and environmental employment in Sweden. Entitled “FEESE” (Fostering…

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Abstract

This paper presents the main results of a research project looking at trends on environmental technology and environmental employment in Sweden. Entitled “FEESE” (Fostering Employment in the Environment Sector in Europe), the project analysed provisions and needs in respect of environmental training among a sample of Swedish companies, which are outlined in this paper. Some recommendations which may be useful to Sweden, but which are also applicable to other industrialised countries, are also presented.

Details

Environmental Management and Health, vol. 11 no. 4
Type: Research Article
ISSN: 0956-6163

Keywords

Article
Publication date: 8 October 2020

Diana Floegel

This paper examines promotional practices Netflix employs via Twitter and its automated recommendation system in order to deepen our understanding of how streaming services…

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Abstract

Purpose

This paper examines promotional practices Netflix employs via Twitter and its automated recommendation system in order to deepen our understanding of how streaming services contribute to sociotechnical inequities under capitalism.

Design/methodology/approach

Tweets from two Netflix Twitter accounts as well as material features of Netflix's recommendation system were qualitatively analyzed using inductive analysis and the constant comparative method in order to explore dimensions of Netflix's promotional practices.

Findings

Twitter accounts and the recommendation system profit off people's labor to promote content, and such labor allows Netflix to create and refine classification practices wherein both people and content are categorized in inequitable ways. Labor and classification feed into Netflix's production of culture via appropriation on Twitter and algorithmic decision-making within both the recommendation system and broader AI-driven production practices.

Social implications

Assemblages that include algorithmic recommendation systems are imbued with structural inequities and therefore unable to be fixed by merely diversifying cultural industries or retooling algorithms on streaming platforms. It is necessary to understand systemic injustices within these systems so that we may imagine and enact just alternatives.

Originality/value

Findings demonstrate that via surveillance tactics that exploit people's labor for promotional gains, enforce normative classification schemes, and culminate in normative cultural productions, Netflix engenders practices that regulate bodies and culture in ways that exemplify interconnections between people, machines, and social institutions. These interconnections further reflect and result in material inequities that crystalize within sociotechnical processes.

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