Policy and Backwardness of Maritime Society Case Study on Community Maritime Affairs Bugis Makassar South Sulawesi
Bibliographic record
Abstract
This research is motivated by a concern to the maritime community in Indonesia and South Sulawesi in particular, which is still very behind compared with other communities on the mainland or degenerate far backward compared with the maritime community in the past royal era (VIII century - XVII century: Sriwidjaya, Majapahit, and Gowa-Makassar). This study aims to reveal the concern of the government on maritime development in the State Bugis Makassar of South Sulawesi. This study uses Verstehen method was conducted through "historical approach" since the period of the kingdom until the reform period. The results showed that the retardation of Bugis-Makassar Maritime Communities in the Bugis-Makassar State was started in inattention government or precisely turned attention both central and local governments from land to sea. Since independence era or period of the republic, government policy in general more focus on the continental policy (to the mainland) in terms of physical potential of this nation is dominant on the maritime potential and this nation has historically also known as the nation's oceans (maritime) than the nation's land. The study recommends to realize what has been painstakingly formulated in Repelita VII up to X, a formula that relies on modernization alignments according to the great potential of maritime nations.
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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 | 0.001 | 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 | 0.006 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".