Bibliographic record
Abstract
This study aims to find out the Maritime Economic Development of ASEAN Countries and Riau Islands - Indonesia. ASEAN which was established on August 8, 1967 through the Bangkok Declaration by Indonesia, the Philippines, Malaysia, Singapore, and Thailand. Then in 1984 he joined the State of Brunei Darussalam, in 1995 followed by the country of Vietnam, in 1997 the countries of Laos and Myanmar, and in 1998 joined the country of Cambodia. ASEAN in the Indonesian language known as Perbara or Perhimpunan Nations of Southeast Asia is a collaborative organization in the field of economy and geo-politics. The variables used in this study are Economic Growth, Export Rate. Inflation, and IPM. The data used is time series data, namely from 2014-2016. The analytical method used in this study is descriptive and econometric analysis. World Bank data, in 2017, predicts that there are three countries, namely Cambodia, Laos and Myanmar, which are predicted to have the most expansive economic growth after India in 2017-2019, and it is estimated that economic growth can reach 7%. while Indonesia in Quarter II 2017 grew 5.1 percent (BPS, 2017), while in 2013 it grew 5.58 percent. Riau Islands, a small town that captures part of NKRI in 2015 6.02 percent (yoy) economic growth, in the second quarter of 2017 must be willing with the lowest number two national economic growth, which is 2.02 percent which was the highest in Sumatra exceed national figures of 4.79 percent. (yoy). Suggestions given in this study include the need to think about a policy strategy that has potential economic areas to support sustainable export growth so that it can improve economic growth better.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".