Migration and Development: The Centrality of Gender Roles in Shaping Remittance Decisions in Botswana and Zimbabwe
Bibliographic record
Abstract
Over the past decade, Zimbabwe has experienced a severe economic and political crisis characterized by hyperinflation and soaring rates of unemployment. Unable to secure livelihoods for themselves and their families, millions of Zimbabweans have travelled to neighboring countries simply to survive. The remittance of money and goods by those who left has become essential not only to household budgets in Zimbabwe but also to the national economy. While migrant remittances contribute to poverty alleviation, their differential impact on male and female migrants and their households is an important factor in assessing the impact of these remittances. Poverty and migration are highly gendered experiences in Zimbabwe: women are disproportionately affected by poverty, and migration within the southern Africa region is increasingly feminized. Drawing upon in-depth interviews with Zimbabwean migrants in Botswana, one of the largest migrant-receiving countries, and those ‘left behind’ in Zimbabwe, this paper highlights the centrality of gender in shaping migration decisions, remittances and their impact on household poverty alleviation. These insights contribute to emerging critical perspectives on the developmentalist assumptions embedded in migration discourses by drawing attention to the gendered dimensions of the costs and benefits at the household level.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.008 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".