Standardization of Indonesia’s Islands Name as an Effort in Safeguarding the Republic of Indonesia Sovereignty
Bibliographic record
Abstract
In its position as sovereign state, the possession of definite territory is a must for Indonesia; as a consequence of its status as an archipelagic state therefore Indonesia has the responsibility in determining the border of its territory in map forms with sufficient scale in affirming its position. In the year 1987, Government of Indonesia submitted a list reporting the increasing amount of island from 13.667 to 17.508 when attending United Nations Conference on Standardization of Geographical Names (UNCSGN) in Montreal, Canada. At that time, United Nations responded in asking Indonesian Government to submit list of the islands to United Nations. Based on December 2007 data, reported that, from 17.504 islands scattered all around, only 6900 islands has name standardization in accordance with international standard. While the rest around 10.600 islands without standardization name which internationally recognized. The paper is aimed to raise the urgency of name standardization for Indonesia’s islands in accordance with the rules of international law and the Indonesian Government’s efforts in standardizing islands names in Indonesia. It is concluded that the efforts in standardization of island names in Indonesia ought to do, so that the remaining islands that become the part of Republic of Indonesia territorial sovereignty has an international recognition. Though there are few constraints faced by the Indonesian government in conducting islands names standardization in Indonesia, such as: lack of coordination between relevant authorities, various numbers of tribes and local languages and limited funds.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".