Konsep Dasar Pendidikan Islam Inklusif : Studi Tentang Inklusivitas Islam Sebagai Pijakan Pengembangan Pendidikan Islam Inklusif
Bibliographic record
Abstract
The paradigm shift in the inclusive Islamic education curriculum is an essential part of Presidential Decree No. 7 of 2021. There is a tendency for religious learning to be normative-indoctrinative and lead to truth claims, raising suspicions that religious education contributes to the generation of extreme views. PPIM UIN Jakarta research shows that the PAI curriculum is still ambiguous on the issue of tolerance, and there is a tendency for PAI teachers to have an intolerance opinion towards minorities by 34%, and towards adherents of other religions by 29%. This study discusses the concept of inclusive education in Islam, the urgency of inclusive Islamic education, and the paradigm shift from exclusive to inclusive. This research is a literature study with a rationalistic approach. Data analysis uses reflective thinking logically to interpret the inclusive values of Islamic education and reflect them into strategic steps to answer the challenge of exclusivity. This study shows that Islam carries an inclusive spirit characterized by terminologies such as at-ta'arruf, at-tasammuh, at-tawassuth, and at-ta'awun. The urgency of inclusive Islamic education is intended so that the character of inclusive Islam is truly taught in learning. To change the paradigm of Islamic education from exclusive to inclusive, improvements are needed in curriculum elements, educators, and learning strategies.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".