The Right to Food Beyond Borders: The Extraterritorial Reach of the Right to Food in Africa
Bibliographic record
Abstract
INTRODUCTION In an increasingly interconnected world, actions or inactions by one state can have an impact on the realisation of rights, including the right to food, for people in another state. The agricultural and trade policies of one state can affect the price of agricultural goods on global markets, with devastating consequences for small-scale producers abroad. Transnational corporations, operating under the regulations of one state but working in another, can damage the environment, evict farmers or convert agricultural land away from food production. This can affect the availability and accessibility of adequate food for local populations. Despite the transnational nature of these consequences, the conventional conception of human rights – one that understands human rights as only applying between a state and those living within its borders – provides no accounting for the impacts of a state's policies abroad. Over the past few years, drawing on the key international human rights instruments, norms and jurisprudence, legal scholars have sought to bring to the forefront the human rights obligations of states that extend beyond their borders. These obligations, most oft en referred to as extraterritorial obligations, require states to take seriously the human rights of people living in other countries when designing polices that may have an impact on them. The right to food, recognised in international law and domestically by a growing number of countries, is the right of all persons to feed themselves in dignity, either by producing or purchasing their food. With respect to the right to food, extraterritorial obligations require that states ensure their policies and actions do not have a negative effect on the ability of people living elsewhere to meet their food needs in dignity and that, in certain situations, states actively assist foreign populations in meeting their food needs. In these ways, extraterritorial obligations offer a very different frame by which to analyse and understand the impact of a state's actions and inactions outside of its borders. This frame is in stark contrast to the historical practice of international relations and cooperation in the area of food security, in which states have aided populations in foreign states with access to adequate food as an act of’ charity’ or, at most, a form of development assistance (oft en self-serving) – and not as a state obligation.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".