Political and Ideological Vulnerabilities in Indonesia's Border with Timor-Leste to Support State Resilience and Security: A Study on Belu and Malaka Regencies
Bibliographic record
Abstract
Border areas are important to discuss, considering their location affects state sovereignty.This research aims to analyze political and ideological vulnerabilities in Belu and Malaka Regencies, which are important areas along the eastern sector of the Indonesia-Timor Leste land border.An analysis of political and ideological vulnerabilities in the region is used in formulating strategies to strengthen the country's resilience and security.This research is survey research with data collection using questionnaires, in-depth interviews and observations with 204 respondents, and integrating academic literature and ArcGIS spatial analysis.Existing findings indicate that Belu and Malaka districts have low political and ideological vulnerability, as evidenced by increased political awareness and strong resistance to ideological attacks, highlighting the importance of government involvement in ensuring territorial integrity through increased bilateral communications and cooperation with Timor-Leste.The research results recommend increasing political awareness through education, encouraging bilateral dialogue, involving local communities in governance processes, fostering intercultural tolerance, improving infrastructure and economic frameworks, and establishing rapid threat response mechanisms.These suggestions aim to strengthen the country's resilience and border security, while facilitating peaceful and long-term bilateral relations.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".