The Peculiarities of International and Domestic Experiences in the Development of Inclusion in Higher Education Institutions
Bibliographic record
Abstract
The publication is aimed at researching the peculiarities of domestic and international experiences in the development of inclusion in higher education institutions (HEI). On the basis of studying the works of scholars, the stages in development of education for persons with special needs in Ukraine are presented; forms of education for students with special needs are presented; differences in the inclusive forms of learning from others are specified. The international experience in the development of an inclusive approach is studied on the examples of universities in Canada, the United States, the European University Viadrina, the University of Oslo, the Masaryk University. The peculiarities of the introduction and implementation of inclusion in the Ukrainian HEI are considered using such examples as the National University «Lviv Polytechnika», the Petro Mohyla Black Sea National University, the National Technical University «Igor Sikorsky Polytechnic Institute of Kyiv», the State University of Sumy. A plan for the development of inclusion in the HEI is formed together with the identification of its main directions and ways of implementation.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.009 | 0.013 |
| Scholarly communication | 0.012 | 0.006 |
| Open science | 0.001 | 0.015 |
| Research integrity | 0.001 | 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".