What Is Meant by Inclusive Education? Perceptions of Turkish Teachers towards Inclusive Education
Bibliographic record
Abstract
The aim of this study was to determine the perceptions of teachers in Turkey towards inclusive education through metaphors. Phenomenology was adopted in this research. The study group of the research consisted of 120 teachers working in public schools in Turkey in 2019-2020 academic year. Snowball sampling, criterion sampling and maximum diversity sampling methods were used to determine the study group. Content analysis was applied to the data. As a result of the research, it was revealed that Turkish teachers considered inclusive education as a process that required patience and exertion and that this education was an indispensable need for the student with special needs. It was concluded that suitable educational support should have been given to the students with special needs according to their individual needs. It was also concluded that it was important for students with special needs to share the same learning environment in an integrity without separating them from their peers who had ‘normal’ academic and social development. In addition, it was emphasized that students with special needs should have been accepted with their own behaviours and characteristics in the learning environments they were in and it was important to turn these differences into opportunities for the benefit of the student.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".