Sustaining urban labour markets: Situating migration and domestic work in India's ‘gig’ economy
Bibliographic record
Abstract
The domestic work sector in India has been absorbing an overwhelming proportion of workers who migrate from rural and semi-urban spaces to cities for employment. The supply of workers is driven by multiple unregulated intermediaries, which expose them to multiple modes of exploitation before and after the point of placement. We compare digital platforms, which have recently entered the sector as intermediaries, to traditional placement agencies as pathways to livelihood opportunities in the domestic work sector. We shed light on the placement routes for domestic workers in the platform economy by comparing it with the larger informalised domestic work sector. We also compare the impact of different types of digital platforms and traditional intermediaries on migrant workers and the supply chain of migration. The analysis is based on qualitative inputs provided by domestic workers in two Indian cities – Delhi and Bengaluru as well as inputs from platforms, unions and government agencies. This primary data when situated in the context of traditional modes of intermediation presents the inadequacies of platforms in overcoming the challenges of the institutional ecosystem for migrant domestic workers. We conclude that the histories of intermediaries and work arrangements in domestic work continue to shape the position of migrants in the platform economy.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| 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".