Management Styles of Special Education Schools Comparison between China and Thailand
Bibliographic record
Abstract
The objectives of this study were to study the current management style of special education schools in China and Thailand, to study the differences in the management of special education schools between China and Thailand, to provide advice for the management of special education schools in two countries. The research used an interview to collect quality data from 30 managers who manage Chinese special education schools and Thai special education schools. Content analysis was used to analyze the data. Special education schools in China and Thailand have similar school culture management, similar administration management structure and function, similar teacher management and similar parents education management. However, there are some differences in the management of teaching instruction management, teaching material management, student management, and teaching equipment management. Compared to Chinese special education schools with Thai special education schools, the advantage of Chinese special education schools is that they can create specialized teaching material suitable for children with mental retardation. It is better to meet students’ actual needs, is more specialized and targeted, and meets the needs of students' development. The advantage of Thai special education schools is that they can provide pre-school education in all schools for children with special needs, provide critical early intervention in the early childhood of special children.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".