Agency and servitude in platform labour: a feminist analysis of blended cultures
Bibliographic record
Abstract
Digital labour platforms have become important sites of negotiation between expressions of micro-entrepreneurship, worker freedom and dignity of work. In the Global South, these negotiations are overlaid on an already fraught relationship mediated by the dynamics of caste and culture, to the usual politics of difference. Urban Company (UC), an app-based, on-demand platform in India that connects service providers offering home-based services to potential customers, lists professionalised services that have hitherto been considered part of a ‘culture of servitude’, performed by historically marginalised groups afforded little dignity of labour. Such platforms offer the possibility of disrupting the entrenched ‘master-servant’ relationship that exists in many traditional cultures in the Global South by their ostensibly professional approach. While service providers now have the opportunity for self-employment and gain ‘respectability’ by being associated with the platform, UC claims to have leveraged AI to automate discipline in everything the providers do. Using interviews with UC women service providers involved in beauty work and software development engineers, this paper explores the agency afforded to service partners in both professional and personal spheres. Further, we propose the term blended cultures to think about the ways in which algorithms and human cultures mutually (re)make each other.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.024 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".