Quality of Early Childhood Education and Care in Kazakhstan: The First Nationwide Study
Bibliographic record
Abstract
Currently, Kazakhstan has the highest enrolment rate in the history of early childhood education and care (ECEC), with 98% enrolment for children aged three to six years old. With this significant expansion of ECEC, there is a lack of sufficient evidence on its overall quality. This study is the first countrywide study aimed to evaluate the ECEC quality in Kazakhstan using the internationally recognized Early Childhood Environment Rating Scale (ECERS-3). We looked at 50 preschool classrooms from all regions of Kazakhstan. The preschools had different combinations of the following characteristics: located in urban/rural areas; state/private; with Russian/Kazakh-language instruction. The scores demonstrated ‘below the minimal’ quality of ECEC in Kazakhstan. No correlation was found between the quality of ECEC and regions or types of settlement. Findings revealed such problems as deprivation of play, predominance of teacher-led pedagogy, large child-to-staff ratio and others. Children are not offered adequate amounts or variety of cognitively stimulating opportunities that would support their development and learning. There was a statistically significant difference in quality depending on the language of instruction. Kazakh groups were more likely to score worse than Russian ones (N=47, p=.026). The reasons for these findings are numerous, both due to the complexity of the ‘quality’ notion, as well as various issues that influence the quality of ECEC in Kazakhstan.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".