Genetically Modified Crops and Gender Relations in Low-and Middle-Income Countries: A Critical Review
Bibliographic record
Abstract
Abstract Since their release in the early 1990s, genetically modified (GM) crops have been lauded as a tool to redress stagnating yields and food insecurity among poor farmers. The potential for GM crops to alleviate poverty for farmers in low- and middle-income countries (LMICs) will likely hinge on their ability to enhance women’s overall well-being, yet there is little research that evaluates if (and how) the technology has such transformative potential. This article reviews the existing scholarship on this topic by grouping it into three strands: (1) the impacts of GM crops on labor processes, (2) gender and patterns of adoption, and (3) the consequences of GM crops for intra-household gender relations. Each area is characterized by contradictory findings, reflecting the diversity and complexity of gender relations in different contexts. Our review suggests that further research should build on mixed-method approaches that involve long-term interactions with households in order to generate robust and gender-disaggregated data that yield nuanced, context-specific analysis.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".