Household Educational Decision-making in Low-fee Private Primary Schools in Kenya: An Exploratory Mixed Methods Study
Bibliographic record
Abstract
Despite the pronouncement of Free Primary Education (FPE) made by the Government of Kenya in 2003, not all households have equally benefitted from its implementation. Children from many poor households, particularly in urban informal settlements, continue to attend fee-charging private schools which have continued to grow exponentially and to figure prominently on Kenya's educational landscape. To address this conundrum as to why poor Kenyan households are using low-fee private schools (LFP) when there is free primary education in the public schools, I apply notions of social capital and habitus to examine household decision-making pathways. Specifically, I ask which households are using low-fee schools, why, and how do these households navigate and negotiate this emerging educational market.In this study I problematize choice processes as a complex interaction between macro-level institutional policy frameworks, meso-level organizational practices, and micro-level decisions. This mixed methods multilevel study was based on a survey of 209 households from one village in Kibera, and involved five target schools, one public and four low-fee schools. Qualitative in-depth interviews at the state level with Ministry of Education officials, donors, and civil society organizations, as well as school level interviews with school proprietors, teachers, pupils and school management committee (SMC) members, and household level interviews with a smaller set of 21 parents were used to triangulate and to complement findings from the household survey. Overall the study found that decision-making for the economically disadvantaged households in this study was differentiated by the social, cultural, and economic capital they own. As households navigate between public and LFP schools, the results suggest that choice is not equitable as not all households are able to fully exercise their right to choose nor, do they have agency. In many cases households are pushed into a decision. Based on these findings, I propose a categorization of households based on their decision-making pathways into two groups: default decision-makers and strategic decision-makers. The analysis also shows that households rely on social networks, and ethnicity considerations in making school decisions. These decisions however, appear to be reinforcing existing boundaries of social class, and exacerbating stratification in the schools.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".