IMPROVING FINANCING MECHANISMS FOR PUBLIC ]SERVICES IN SECONDARY EDUCATION
Bibliographic record
Abstract
Abstract. This article examines and proposes improvements to the mechanisms of financing public services in secondary education in the Republic of Kazakhstan in the context of modernization and digital transformation. The relevance of the study is driven by the need to enhance the efficiency of public spending, reduce regional disparities, and ensure equitable access to quality education. The methodological framework combines regulatory analysis, comparative assessment, economic and statistical methods, expert evaluation, and content analysis of strategic documents. The study identifies several systemic challenges: insufficient differentiation of per-capita funding norms based on regional conditions, a high share of small rural schools in northern and eastern regions, limited digitalization of financial monitoring processes, and low flexibility of budget procedures, which constrains effective resource management at the school level. The findings indicate that unified budgeting standards do not reflect actual demographic, infrastructural, and economic differences across regions, resulting in unequal funding allocation and reduced quality of educational services. The results were compared with international experience from OECD countries, including Finland, Estonia, Canada, and South Korea, which apply differentiated funding models and advanced digital monitoring systems. Based on the empirical findings, several policy recommendations are proposed: the introduction of adjustment coefficients, expansion of school financial autonomy, full digitalization of financial processes, and a transition to performance-based funding models. The conclusions of the study offer practical value for enhancing financial policy in the field of education.
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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 | 0.013 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".