The link between poverty and malnutrition: A South African perspective
Bibliographic record
Abstract
In this article, a brief review of the nutritional problems in South Africa, as well as the intergenerational, vicious cycle of poverty and malnutrition, are used to argue for the necessity of including a nutrition intervention component in poverty alleviation programmes. It is concluded that this cycle can be broken by improving the nutritional status of women in their productive years, whereby foetalmalnutrition, arrested mental development and physical stunting in children, adolescents and adults can be prevented. The result will be an improvement in human capital, health and productivity with the ultimate aim of escaping poverty as suggested by the seven principles of Solomons(2005).OpsommingIn hierdie artikel word ’n kort oorsig van die voedingsprobleme in Suid-Afrika sowel as die noodlottige siklus van wanvoeding en armoede wat oor generasies strek, gebruik om aan te voer dat dit noodsaaklik is om ’n voedingsintervensie-komponent in programme gemik op die verligting van armoede in te sluit. Daar word tot die gevolgtrekking gekom dat die siklus gebreek kan word as die voedingstatus van vroue in hulle voortplantingsjare verbeter word. Hierdie verbetering sal fetale wanvoeding, sowel as belemmerde groei en kognitiewe ontwikkeling van kinders, adolessente en volwassenes voorkom. Die gevolg sal ’n verbetering van menskapitaal, verbeterde gesondheid en verhoogde produktiwiteit wees, met die uiteindelike doel om armoede te ontsnap soos voorgestel deur die sewe beginsels van Solomons (2005).
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".