Assessing Awareness and Perceptions Towards the Existence of Indigenous Foods in Port St Johns of the Eastern Cape South Africa
Bibliographic record
Abstract
Intolerably high rates of food insecurity and micronutrient deficiencies still prevail at an alarming rate in rural poor communities who practice subsistence farming. Despite the fact that, the indigenous fruits and vegetables are abundantly available and are easily accessible in these rural communities. The consumption of indigenous vegetables and fruits can combat the food insecurity and micro-nutrient deficiencies in the resource-constrained communities. This is attributed to negative perceptions shared among rural communities specifically the younger generation are unaware about the indigenous foods. Against this background, the study was developed to assess awareness and perceptions towards indigenous fruits and vegetables in Port St Johns of the Eastern Cape Province of South Africa. Intolerably high rates of food insecurity and micronutrient deficiencies still prevail at an alarming rate in rural poor communities that practice subsistence farming. Even though indigenous fruits and vegetables are abundantly available and are easily accessible in these rural communities. The consumption of indigenous vegetables and fruits can combat food insecurity and micro-nutrient deficiencies in resource-constrained communities. This is attributed to negative perceptions shared among rural communities, specifically the younger generation, who are unaware of indigenous foods. Against this background, the study was developed to assess awareness and perceptions towards indigenous fruits and vegetables in Port St. Johns of the Eastern Cape Province of South Africa. A multi-stage sampling technique was used to evaluate the availability of the perceptions of households and the contribution of indigenous fruits and vegetables to household food security. A total of 340 respondents were purposively selected in the study area. A positive impact on household food security was revealed, suggesting that consuming indigenous fruits and vegetables may address rural household dietary diversity and food insecurity. The study argues that indigenous fruits and vegetables may be used as a food security coping strategy at the household level in rural areas, given their availability, especially in summer. Additionally, dispelling several negative perceptions and targeting consumption drivers will enhance the food security nexus of indigenous fruits and vegetables at the household level.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".