“Empowerment” as efficiency and participation: gender in responsible agricultural investment principles
Bibliographic record
Abstract
Amidst global concern over the state of transnational large-scale agricultural investments, several efforts have been made to set global standards for “responsible agricultural investment.” While these efforts have received mixed reviews from the international community, very little attention has been paid to the gendered language of these principles. Through examining two separate sets of agricultural investment principles – one created by the United Nations Food and Agriculture Organization (FAO), the United Nations Conference on Trade and Development, the International Fund for Agricultural Development and the World Bank, and the other by the United Nations Committee on World Food Security – this article finds that, despite different processes and participants in the creation of these principles, they nonetheless share a language of “empowerment” targeted at women and marginalized groups. However, in contrast to early feminist discourses of empowerment, these principles instead perpetuate the notion that empowerment is to be found through efficiency, productivity and participation in land and labor markets. This article takes a critical look at this language of economic empowerment in each set of principles, and points to the dangers of equating efficiency, productivity and participation with feminist empowerment. By not acknowledging the broader gender dynamics of agricultural governance and markets, these discourses risk deepening existing inequalities rather than moving toward meaningful social change.
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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 | 0.016 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.062 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.003 | 0.004 |
| 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".