Practice and Innovations of Inclusive Education at School
Bibliographic record
Abstract
The article has carried out a meta-analysis of the research concerning practice and innovations of inclusive education at school. Investigation of the practice of inclusive education at schools has been intensified since the 1990s, after identifying the need to implement inclusion strategies and concepts at the international level. The first studies of inclusive education (until the 2000s) concerned beliefs and values as a factor, influencing the effectiveness of inclusion, strategies of inclusive education. Investigations after the 2000s have been aimed at more focused subject matter of the research at the local level in different countries: principals’ beliefs, teachers’ self-efficacy, the role of parental support, school ideology, models of inclusion at private schools, the severity of disability as a factor determining teachers’ beliefs concerning inclusion. Various inclusive models have been formed as a practice result of implementing inclusion. Two key effective approaches to integration of inclusion have been highlighted: integrated and differentiated. An integrated approach involves the introduction of innovations in inclusive education in the following elements of the educational system, namely: the concept (strategy) that defines the model, external preconditions and stages of inclusion; a school that defines the internal prerequisites for inclusion; a community. A differentiated approach is used in combination with theintegrated one in order to identify the internal prerequisites for inclusion: values, beliefs and attitudes of teachers, the competence of educators.
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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 | 0.066 | 0.161 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.008 |
| Bibliometrics | 0.012 | 0.007 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".