Opinions and Recommendations of Academics Training Special Education Teachers About Applicability of Integration in Turkey
Bibliographic record
Abstract
This study aims to state the opinions and recommendations of academics who train special education teachers for integration applications. The study is patterned as a descriptive case study, and the participants consist of 28 faculty members from 18 universities who work in the special education undergraduate program and whose professional experience ranges from 5 to 45 years. The study data were collected through face-to-face interviews with participants using the semi-structured interview technique. The obtained data were analyzed through descriptive analysis. The findings show that most academics think that integration is not being applied today and that there is a lack of necessary infrastructure for its application. Faculty members participating in the study stated that teachers do not receive adequate training about integration, teachers should undergo serious in-service training, and courses related to special education should be introduced in all departments of the faculty of education. For successful integration, it is necessary to make physical and educational arrangements, provide support education services, train teachers to have an integration perspective and improve their attitudes. In conclusion, integration is an application that can only be realized with the joint effort of all parties through multidimensional discussions of its contents.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".