Getting home during lockdown: migration disruption, labour control and linked lives in India at the time of Covid-19
Bibliographic record
Abstract
This paper uses the Covid-19 induced migration disruption in India as a lens to interrogate what this acute moment reveals about the precarity of India’s migrant workers and their experiences of work in ordinary times. Interviews with interstate migrants from north India employed in the Tiruppur region in the southern state of Tamil Nadu present their narratives of being stuck at work when lockdown began, their subsequent struggles to get home, and finally their plans to return to Tamil Nadu later in 2020. Migrant accounts of migration disruption shed light on (1) the local labour control regime at destination that routinely keeps interstate migrants locked into highly exploitative work environments, and that was intensified during lockdown, and (2) the ways in which this labour regime thrives on the spatio-temporal separation of productive and reproductive spheres in migrants’ linked lives. The Covid-19 disruption also reveals how this labour regime flexibly adapted to produce the simultaneous disposability and unfreedom of circular migrant workers. Drawing on critical literature on labour control regimes, and on the separation of productive and reproductive labour under contemporary capitalism, we show how the Covid-19 pandemic disruption was anything but a transformative moment for India’s vast circular migrant workforce.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.015 | 0.012 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.001 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".