Food Sovereignty and Rights-Based Approaches Strengthen Food Security and Nutrition Across the Globe: A Systematic Review
Bibliographic record
Abstract
This systematic review assembles evidence for rights-based approaches–the right to food and food sovereignty–for achieving food security and adequate nutrition (FSN). We evaluated peer-reviewed and gray literature produced between 1992 and 2018 that documents empirical relationships between the right to food or food sovereignty and FSN. We classified studies by literature type, study region, policy approach (food sovereignty or right to food) and impact (positive, negative, neutral, and reverse-positive) on FSN. To operationalize the concepts of food sovereignty and the right to food and connect them to the tangible interventions and practices observed in each reviewed study, we also classified studies according to 11 action types theorized to have an impact on FSN; these included “Addressing inequities in land access and confronting the process of land concentration” and “Promoting gender equity,” among others. We found strong evidence from across the globe indicating that food sovereignty and the right to food positively influence FSN outcomes. A small number of documented cases suggest that narrow rights-based policies or interventions are insufficient to overcome larger structural barriers to realizing FSN, such as inequitable land policy or discrimination based on race, gender or class.
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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 | 0.007 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 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".