Bibliographic record
Abstract
The background of this scientific paper is the author’s awareness of Indonesia's crude palm oil production which is abundant and the potential to expand its sales to a larger scale. But its potential to grow has to face many problems in the development process. The most severe constraint that is felt by the government of Indonesia is the black campaign on Indonesian palm oil by European countries. Palm oil produced by Indonesia is not considered environmentally friendly and causing natural damage in the plantation process. Yet, in the reality, Europe countries still use crude palm oil from the countries of themselves. This case according to international relations science can be regarded as EU’s effort of protectionism against palm oil production by another country. Protectionism is the act of a country formulating economic policy in such a way in order to protect the domestic economy from the domination of foreign products, thus requiring different powers of government that affect trade patterns and location of economic activity globally. To deal with this policy, Indonesian government must take some serious actions to minimize the occurrence of protectionism done by other countries. The same protectionism effort has also been faced by the Canadian government through the meat import that has been declined by America due to their protectionism policy. Indonesian government can carry out Canadian rescue mechanism against protectionism as a model to face European practice of protectionism. The effort is to do forum shopping to choose the right legal framework to address these issues. In addition to efforts by forum shopping, Indonesia can also make a positive campaign about the advantages of palm oil production in cooperation with the epistemic community. These things need to be done by the government of Indonesia to rescue the production of palm oil
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 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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.040 | 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 teacher head, 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".