ENHANCING THE ECONOMIC EFFICIENCY OF PUBLIC SERVICES THROUGH THE IMPLEMENTATION OF INCLUSIVE POLICIES
Bibliographic record
Abstract
In the context of increasing socio-demographic complexity and rising demand for equitable public services, inclusive policy has become a strategic priority in public administration. This study evaluates the economic efficiency of integrating inclusive approaches into Kazakhstan’s public service system, with a particular focus on services for children with special needs. The research is based on international comparative analysis, statistical data, and economic modeling. Theoretical frameworks such as Social Justice Theory, New Public Management, and Participatory Democracy Theory serve as the foundation. Evidence from Canada, Finland, Germany, and Japan demonstrates that inclusive policy reduces healthcare costs, expands labor market participation, and generates long-term returns on investment through GDP multiplier effects. In Kazakhstan, the number of children with special needs increased by 65.8% between 2020 and 2024, highlighting the urgency of reforms in education, healthcare, and social protection. Economic models developed in the study quantify savings in healthcare expenditures, potential growth of tax revenues, and projected demand for public services. The results indicate that inclusive governance is an effective mechanism for reducing social inequality while ensuring fiscal stability and institutional resilience. The proposed framework offers practical, evidence-based instruments for designing, evaluating, and optimizing inclusive public service delivery. This approach strengthens the foundations of sustainable development and supports long-term national competitiveness.
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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 | 0.017 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".