WAYS TO EFFECTIVELY IMPLEMENT INCLUSIVE EDUCATION IN WORLD EXPERIENCE AND IN UZBEKISTAN
Bibliographic record
Abstract
Inclusive education, which ensures quality education for all children regardless of ability, is a global priority, yet its implementation faces challenges, particularly in low- and middle-income countries like Uzbekistan. This article explores world experiences in inclusive education and proposes strategies for effective implementation in Uzbekistan, focusing on policy frameworks, teacher training, and infrastructure development. Globally, inclusive education improves learning outcomes for all students, with countries like Norway achieving 95% mainstream inclusion through personalized pedagogy. In Uzbekistan, where children with disabilities constitute 75% of those in institutional care and 9,700 remain out of school, the government aims for 51% of schools to adopt inclusive models by 2025. This study analyzes data from 225 inclusive schools piloted under the 2020–2025 Presidential Decree, revealing 80% teacher training coverage but only 20% of schools with accessible facilities. Key risk factors include limited teacher preparedness (51% report discomfort with inclusive practices) and rural disparities (70% of out-of-school children in rural areas). UNICEF-supported programs, training 10,000 teachers in 2024, have increased inclusive enrollment by 30%. The article proposes adopting international best practices, such as Norway’s high-expectation model and Canada’s universal design for learning, to enhance Uzbekistan’s inclusive education system, reducing exclusion and fostering equitable learning outcomes.
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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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.005 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".