Implementation of Multicultural Values in Islamic Religious Education Based Media Animation Pictures as Prevention of Religious Radicalism in Poso, Central Sulawesi, IndonesiaImplementation of Multicultural Values in Islamic Religious Education Based Media Animation Pictures as Prevention of Religious Radicalism in Poso, Central Sulawesi, Indonesia
Bibliographic record
Abstract
This study aims to promote multicultural values in the culture of Sintuwu Maroso the Poso community through the transformation of education which is integrated with Islamic religious subjects based on animated image media as prevention of radicalism in the school and community environment. Research methods is a qualitative descriptive study in the form of writing and oral words from people who are the object of research, in conducting research, researchers as the main instrument whose role is to observe observations of documents and conduct interviews with informants, analyze data, interpret data and interpret data compiled a report on tracking the value of local wisdom on the multicultural value of Sintuwu Maroso in the Poso community. Islamic religious education learning that is integrated between Islamic religious material and cultural material of the Poso Sintuwu Maroso community can produce students who are always obedient to their God, behave well with others and are far from understanding religion and radical religious behavior in multicultural / change communities. student characters. Islamic religious education learning that is integrated between Islamic religious material and cultural material of the Poso Sintuwu Maroso community can produce students who are always obedient to their God, behave well with others and are far from understanding religion and radical religious behavior in multicultural / change communities. student characters
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".