SOCIALIZATION STRATEGY TO INSTILL NATIONALISM IN 3T (FRONTIER, LEAST DEVELOPED, OUTERMOST) REGIONS TO COUNTER THE NATIONAL DEFENSE THREATS
Bibliographic record
Abstract
The 3T region (terdepan, tertinggal, terluar or frontier, least developed, outermost) in Indonesia is an area that is prone to non-military threats, especially ideological threats that can affect national defense. Various efforts have been made by the government but the level of success achieved is still minimal. In this case, the state needs to seriously develop steps to socialize the attitude of nationalism, especially to people in the 3T region. The purpose of this study was to explore the appropriate socialization strategy used in inculcating the attitude of nationalism in the 3T society regions. This study used qualitative methods with data collection through library research and analytical methods using descriptive analysis. The results of this study are expected to find the right socialization strategy that should be applied in the 3T region in the future. The success in efforts to socialize the planting of nationalism in the 3T region by the government depends on many factors, and the involvement of the central government and regional parties, as well as all levels of society, is an important aspect in determining the success of these efforts.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.003 |
| 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".