QUALITY OF PHYSICAL ENVIRONMENT IN PRESCHOOLS IN INFORMAL SETTLEMENTS IN NAIROBI CITY COUNTY IN KENYA: IMPLICATIONS ON CHILDREN’S DEVELOPMENT AND EDUCATION
Bibliographic record
Abstract
The quality of the physical environment in a school for young children enhances their development and education.This is because it makes them feel safe, comfortable, increases their concentration, reduces absenteeism and leads to better child health.The Kenya Basic Education Act of 2013 and Early Childhood Development Service Standard guidelines of 2006 state that there should be appropriate facilities in educational institutions for young children.The policy documents further provide that facilities in early childhood programmes should meet standards such as adequacy, durability, safety and userfriendliness in order to enhance children's development and education.Despite the policies being in place, the provisions are yet to be fully implemented in preschools in informal settlements.This paper presents results from a study conducted in preschools in informal settlements in Nairobi City County, Kenya.The preschools offer alternative care and education for children who cannot access public preschools, complementing the effort of the county government in providing early childhood education.The study aimed to explore the quality of physical environment in preschools in the informal settlements and pinpoint implications on children's development and
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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 | 0.000 | 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".