A feminist political ecology of agricultural mechanization and evolving gendered on-farm labor dynamics in northern Ghana
Bibliographic record
Abstract
Although agricultural mechanization is central to the renewed agenda for achieving an African Green Revolution, the increased deployment of mechanized technologies has been without critical analysis of the impacts on traditional agrarian labor division practices. Drawing on the experiences of smallholder farmers (n = 60) in northern Ghana using in-depth interviews, we examined the gendered labor implications of agricultural mechanization and how women and men may be responding to evolving on-farm labor dynamics. Our findings reveal a skewed deployment of mechanized technologies in favor of the culturally ascribed on-farm roles of men. This situation has produced a disproportionate labor burden on rural women who are compelled to endure manually in their non-mechanized culturally ascribed roles of sowing and harvesting even as farms are expanding. Although, generally, rural women bear the brunt of these incipient labor demands, certain intersecting vulnerabilities such as belonging to a monogamous household and having fewer or no female children tend to worsen the plight of some women. While gendered labor substitution could balance the disproportionate workload on women, the prevalence of strict culturally constructed gendered labor norms forestalls this potential. Given the painful routine choices rural women make to balance household labor demands, we highlight the need for gender-sensitive mechanization models and policy approaches that address prevailing social inequalities.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".