Bibliographic record
Abstract
Indonesia Maritime Culture, as the first pillar in the concept of maritime axis, is being pursued to be realized by the Government of Indonesia through the maritime curriculum in schools. In fact, Indonesia is experiencing delays in the implementation of marine education (maritime education) compared to countries that have other long shorelines, such as Canada, Japan and the UK. Knowledge of the sea becomes a starting point in marine education (learning and teaching of ocean and aquatic science). This knowledge of the sea is universally agreed upon as ocean literacy, which can be nurtured in marine education. Very little publication of the results of thought and research on Indonesian ocean literacy in national journals, and none in international journals is evidence of Indonesia has not been serious in marine education. In the next maritime curriculum, all subjects required can be integrated with the ocean science. While the subjects closest to ocean science are science learning, especially biology, then geography, physics, and chemistry. Research trends in science learning and teaching in the future should also be oriented to ocean literacy. In addition, socio-scientific issues are also found in marine and coastal life, so student must master of ocean literacy is absolutely done. The study in this article suggests using a system-based approach in teaching science based on ocean literacy. While systemic thinking is the ability to understand and interpret complex systems, and consists of different types and levels of thinking skills. The next suggestion is the application of teaching methods that facilitate systemic thinking skills. Three main suggestions are also given to the marine education community. While the instructional tool that can be adopted to implement marine education is the Ocean Literacy Scope and Squence for Grades K-12. Thus, it is expected that the Indonesian people will be ocean literate.
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 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".