MOODY'S RATING FOR PALM OIL PLANTATION COMPANIES IN MERAUKE, PAPUA
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Bibliographic record
Abstract
Papua is one of the regions that currently receives a lot of investment in plantations and palm oil commodity processing, which previously only focused on Sumatra and Kalimantan. One of the reasons for investment in agribusiness to attract investors and the government is the contribution of the agricultural sector to Gross Domestic Product (GDP), which is around 13.96% in the third quarter of 2017, so that the agricultural sector is one of the second largest contributors to GDP after the manufacturing industry. The agricultural sector is dominated by the plantation sub-sector, where the largest plantation production in Indonesia is palm oil, and Indonesia is the world's largest exporter of palm oil. The objectives of this research are to find out whether investment in oil palm plantation and processing in Papua falls into the "investment" category in Moody's rating and to find out how to make investment in plantations and oil palm processing in Papua fall into the "investment grade" and / or category. can increase the rating through Moody's. This study uses a quantitative research approach. Participants in this study used a purposive sampling technique, where the data collected was obtained from primary data and secondary data. Analysis of the data used in this study is Moody's rating analysis. Data processing is carried out by conducting a spreading assessment of the company's financial statements for the last 3 years to obtain values for historical ratio assessment variables and balance sheet factors, as well as by conducting an assessment of industry / market, company and management variables. After all the input and analysis is carried out, the output is obtained in the form of an investment feasibility rating "B2" with the risk category "Medium Risk". Thus, the company is classified as "investment grade" or feasible for investment, but the B2 score is included in the lowest investment grade category, so improvements are needed so that grading increases and attracts investors. For future researchers, it is advisable to conduct research on a wider sample coverage and emphasize corporate actions that must be carried out.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it