Distress migration: A manufactured dystopia during COVID-19 and lockdown in India
Bibliographic record
Abstract
The movement of daily wage earners and labourers has been cited in most research texts and studies as a “reverse migration” at best, or still worse, likened to the annual circular or seasonal migration seen in India. This study debunks all such theories and seeks to establish the same as distress migration caused as a result of the sudden lockdown imposed by the Indian government and the mishandling of the situation by the administration. This study is a systematic review that has researched existing studies on migration during the pandemic. Using content from news reports and selected studies it exposes the conditions before, during and after the lockdown and the mishandling of the situation on part of the administration that resulted in the massive migrant exodus. This distress migration was affected in response to the manufactured dystopia created by the sudden lockdown and other conditions during the first wave of COVID-19. It also cites data and facts to support the same. According to data estimates, in the quarter prior to the lockdown, 182 million individuals either had employment or were looking for work, and during the lockdown, 7 million people left the labour force. The study establishes that it was indeed distress migration, and not circular or reverse migration, stoked by wrong decisions of a sudden lockdown and manufactured dystopia which resulted in thousands of migrants walking back to their homes for want of any conveyance. Laws that could have been invoked to manage the situation are either defunct or were ignored. While establishing the conditions that led to this distress migration through a systematic review of several studies and also factual content drawn from news report and analysis curated from the period, the study concludes by creating a framework for the future, and suggesting policy measures such as the need for a National Draft Policy on migration, Annual Migrant Survey and a pa-India Migrant Smart Card to keep track of the movement of migrants from one state to another.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".