A Study about Pork Farm in Surakarta as Export Commodity
Bibliographic record
Abstract
Pork as one of most consumed meat in the world had a big potential to be on prospective international trade commodities. Asia, especially East Asia and Southeast Asia had a deep root culture of pork culinary even in Moslem-majority countries like Indonesia and Malaysia. Although its commodity price is considered low in the international trade, the demand of pork is relatively stable. Current top exporter of pork is being held by European Union, followed by United States and Canada, while the current top importer of pork is held by China followed by United States. Southeast Asia actually had a potential to develop its pork export and take some of international market but in reality Southeast Asian countries still struggling to develop their pork export. Between Southeast Asia countries only Thailand and Vietnam who are able to export their pork meat to other countries in a large scale. Therefore, we want to share the result of our qualitative study regarding this topic to enhance the knowledge about the condition and the prospect of this commodity and what barrier that hinder Southeast Asian countries from developing their pork market by using competitive advantage theory and pork farm in Surakarta, Indonesia as the study case. The results of our study are expected could be used as reference to develop pork export in Southeast Asia.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".