Conflict between Groups of Different Religion and Beliefs Posing as Threat to Heterogeneity in Indonesia
Bibliographic record
Abstract
After the 1998 political reform in Indonesia, conflicts between groups of different religions and beliefs continued to occur, regardless of the fact that attempts to bolster diversity have been carried out legally and formally by the government and the political elites. In view of such condition, this research attempts to disclose conflicts which increasingly pose dangers on national heterogeneity, various factors which create religious-based conflicts, the roles of government and political elites in handling such conflicts and the communication strategy adopted to establish a civilized heterogenous society. The research methodology is qualitative with its main focus on online data related with conflicts in Indonesia. Online data processing was performed to support the description of conflicts based on religions and beliefs in all its forms which potentially threat national unity in Indonesia. The findings of this research are as follows: Increasing frequency of conflicts, powerplay politics as fuel for conflicts, unoptimized roles of the government and political elites and lack of communication strategy substance between groups by those responsible for public security and welfare.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".