PulseExploreJournal ClubDebatesTrendingResearchersJournals
Instagram
HomeExploreJournal ClubTrending
Synapse
⌘+K
Synapse
September 10, 2025Science Technology & Human Values0 citations

Friction and Promise in Data Labor

View Full Paper
MRMinna RuckensteinTLTuukka Lehtiniemi

Key Points

  • Friction highlights the complexities in how data labor is shaped by penal policies within a Nordic welfare state, showing varied outcomes.
  • The study reveals how penal policies both support and challenge the perception of data labor as a uniform future development.
  • Applying a friction lens uncovers the human aspects and aspirations within the infrastructure of data labor, indicating potential pathways.
  • Friction transforms into a future-oriented concept, suggesting varied evolutions of data labor rather than a singular global perspective.

Abstract

This article develops friction as a methodological lens and mobilizes it to examine an unusual data labor arrangement in Finnish prisons. The concept of friction highlights how penal policies in a Nordic welfare state both support and intervene in tendencies to view data labor as a uniform future development. While the friction lens draws attention to infrastructural arrangements and institutional forces, it also foregrounds human involvement, imaginaries, and aspirations. Translating aspirations into institutionally rooted practices requires effort and resourcefulness, ultimately producing a “homegrown” version of data labor. The prison offers pushback against how the global data labor infrastructure attempts to reconfigure the human. We demonstrate that the friction lens provides a novel way to analyze data-based automation from a critical perspective, without collapsing differences or overlooking the potential for hopeful pathways. This turns friction into a future-oriented concept that opens multiple views on how things might evolve.

Ask AI
Helpful
Bookmark
Share
View Full Paper

Cite This Study

Ruckenstein et al. (2025) studied this question.

synapsesocial.com/papers/68c1ad5554b1d3bfb60e5415https://doi.org/10.1177/01622439251358900
Ask AI
Helpful
Bookmark
Share
View Full Paper