Maritime History and Culture in Indonesia: Implementation in Social Studies Learning
Bibliographic record
Abstract
This research explores the integration of Indonesia's maritime history and culture into Social Studies education. Indonesia, as the world's largest archipelagic nation, possesses a rich maritime heritage and diverse culture that significantly shape national identity and promote sustainable economic development. The study delves into Indonesia's historical maritime background, including its pivotal role in global trade routes and cultural development. It encompasses maritime kingdoms like Srivijaya, Majapahit, and Makassar as well as the influences of China, India, and the Arab world on maritime culture and trade. The article highlights Indonesia's maritime cultural traditions, fostering appreciation for cultural diversity, environmental sustainability, and responsible attitudes towards marine resources. Various methods, including field research, multimedia resources, and interdisciplinary collaborations, can be employed to implement Indonesia's maritime history and culture into IPS education. This integration enables students to develop a holistic understanding of society, economy, and marine environments. By incorporating Indonesia's maritime heritage, students gain an understanding of marine conservation and sustainable economies, while embracing local wisdom applicable to their daily lives. Ultimately, this implementation fosters a generation that values marine environments, respects cultural diversity, and contributes to sustainable development, providing a solid foundation for understanding history, culture, and the significance of maritime affairs in Indonesia. Keywords: maritime history, maritime culture, social studies, sustainability
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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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".