Implementation and Sustainability of Inclusive Education Programs Towards Curriculum Development Framework
Bibliographic record
Abstract
This study proposed a Sustainability Curriculum Framework for Inclusive Education, addressing the need for research-based and structured educational guidelines. To address the challenges in achieving Inclusive Education, the researcher created this to promote equality and equity for all students in terms of learning opportunities. The purpose of this study is to develop a sustainability curriculum framework for inclusive education that is customized to the Philippine Educational system context. Its focus is on the systemic barriers and the different theories fused into one: the Socio-Emotional Learning Theory, the Social Model of Disability, and the Integrated Curriculum Model. A Mixed-Methods approach was employed, combining quantitative analysis from the Likert Scale Questionnaire responses and qualitative data from the Focus Group Discussions with School Heads. The respondents of this study were purposely chosen. There were 304 school heads from 24 schools across the Luzon Island. The school heads include principals, school directors, master teachers, grade-level chairs, department and subject area coordinators, and moderators of public and private schools in various areas of Luzon. Findings from the study revealed that inadequate teacher training, resource limitations, and policy gaps are hindrances to the effective implementation of Inclusive Education.
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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 | 0.022 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.009 |
| 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".