Responsiveness of the African Continental Free Trade Agreement to diet-related non-communicable diseases: A human rights analysis
Bibliographic record
Abstract
Non-communicable diseases are the leading cause of death world-wide and have increased significantly on the African continent. Changing diets are a significant contributor where people are given more access to unhealthy foods. Trade liberalisation has been revealed as a structural driver of the so-called 'nutrition transition'. There have been calls to take a human-rights based approach to trade law, and specifically in the context of food, to protect diets under the rights to food and health. In 2008 the Special Rapporteur on the Right to Food proposed practical steps to introduce a human rights lens to trade law which can protect food environments. This article measures the African Continental Free Trade Agreement against these proposed measures. It focuses on AfCFTA due to its potential to impact on inter and intraregional trade. AfCFTA also recognises the importance of human rights in a trade law context as part of the text of its establishing agreement. an obligation indeed rests on the South African legislature urgently to implement similar laws to save the lives and protect the various other rights of unsafely abandoned infants. It is proposed that 'baby savers' and 'baby safe haven laws' urgently should be introduced in South Africa to prevent further deaths through the unsafe abandonment of infants in places such as toilets, pit latrines and open fields.
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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 | 0.015 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 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".