Indonesia's Defense Diplomacy Strategy in Facing China's Gray Zone in the South Chine Sea
Bibliographic record
Abstract
Purpose: This study aims to identify an effective strategy for Indonesia to counter China's gray zone tactics in the South China Sea. Theoretical Reference: Employing a quantitative method with a descriptive approach rooted in the positivist philosophy, this research follows the principles outlined by Sugiyono (2011). This method is chosen for investigating specific populations or samples, with a focus on quantitative/statistical data analysis to test predetermined hypotheses. Method: The research employs multiple regression analysis to assess the significance of Soft Power Diplomacy and Smart Power Diplomacy strategies as essential national resources for persuading China to undertake desired actions in addressing the challenges posed by China's Gray Zone in the South China Sea. Smart power, as defined by Jr J.N. (2007), involves integrating soft and hard power for a comprehensive strategy. According to R. Wilson (2008), smart power effectiveness lies in combining a robust military force with investments in alliances, partnerships, and cooperation. Result and Conclusion: The findings indicate that both Soft Power Diplomacy and Smart Power Diplomacy are influential tools and strategies within Indonesia's foreign policy context to effectively navigate and counteract China's Gray Zone activities in the South China Sea. Implications of Research: The identified strategies hold significant implications for shaping Indonesia's defense diplomacy against China's gray zone tactics, emphasizing the importance of integrating soft and hard power elements for a more comprehensive and efficient approach. Originality/Value: This research contributes to the field by highlighting the relevance and effectiveness of Soft Power Diplomacy and Smart Power Diplomacy as key components in Indonesia's defense diplomacy strategy, providing a nuanced understanding of countering China's Gray Zone activities in the South China Sea.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".