The Gendered Consequences of COVID-19 for Internal Migration
Bibliographic record
Abstract
Scant evidence exists to identify the effects of the pandemic on migrant women and the unique barriers on employment they endure. We merge longitudinal data from mobile phone surveys with subnational data on COVID cases to examine whether women were left more immobile and vulnerable to health risks, relative to men, during the pandemic in Kenya and Nigeria. Each survey interviewed approximately 2000 men and women over three rounds (November 2020-January 2021, March-April 2021, November 2021-January 2022). Linear regression analysis reveals internal migrants are no more vulnerable to knowing someone in their network with COVID. Rather, rural migrant women in Kenya and Nigeria were less vulnerable to transmission through their network, perhaps related to the possible wealth accumulation from migration or acquired knowledge of averting health risks from previous destinations. Per capita exposure to COVID cases hinders the inter-regional migration of women in both countries. Exposure to an additional COVID case per 10,000 people resulted in a decline in women's interregional migration by 6 and 2 percentage points in Kenya and Nigeria, respectively.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".