Internalizing the Culture of Religious Tolerance in Children: Advancing the Right of Religious Freedom in Indonesia
Bibliographic record
Abstract
This article aims to determine the exemplary efforts to mitigate the intolerance of Indonesia that spreads among children based on the cultural values of the Bugis Tribe, including Sipakatau, Sipakainge, and Sipakalebbi, as an effort to ensure the right to freedom guaranteed in the 1945 Constitution and the International Covenant on Civil and Political Rights. This type of legal-normative research is descriptive-prescriptive, focusing on examining primary legal materials and legal materials, using statutory, conceptual, and historical approaches. The results of the study show that the cultural values of Sipakatau, Sipakainge, and Sipakalebbi, which are the value system or way of life of the Bugis tribe, contain the meaning of religious tolerance, namely Sipakatau implies mutual respect for God's creation; Sipakinge, reminding each other not to do things in the form of disrespect for people of different religions; and Sipakalebbi with the meaning of loving fellow-creatures regardless of religion. Based on its authority, the Indonesian government should formulate policies to internalize tolerance based on the cultural values of Sipakatau, Sipakainge, and Sipakalebbi. This can be done by referring to the findings as a sociological formulation of regional regulations regarding early childhood education curriculum.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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".