An analysis of the patterns of food consumption among households in Kenya
Bibliographic record
Abstract
The study of food consumption patterns is important in improving the welfare of the population of Kenya. The study, on which this paper is based, analyzed the consumption of different foods among households using data from the Kenya Integrated Household Budget Survey carried out in 2005-2006 in Kenya. The study also compared this to data from the Food and Agricultural Organization, for selected years, for the period 1965 to 2002 in Kenya. It was found that cereals constituted the most popular food consumed. Other important foods consumed were fruits, pulses, vegetables, milk and eggs. Meat and sugars were also represented among the foods well liked. It is therefore important for the government to implement policies to improve dietary food intake to assist the population, especially the poor. This paper is significant because, by identifying the predominant foods consumed by the population, the problem of access to food can be addressed by the government. This paper therefore contributes useful knowledge on the problem of food access as well as providing additional insight into the food and dietary patterns of the population in Kenya. This enables researchers to assess clearly the effects of past and present food policies and enables a study of how effective they have been in alleviating poverty and improving access to food.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".