Urgency of Establishing a National Spices and Herbal Agency In Realizing Golden Indonesia 2045
Bibliographic record
Abstract
Indonesia has a very large potential for "spices and herbs." Both are abundant as one of the natural resources bestowed by God Almighty. These resources have strategic value in the fields of health, medicine, industry, beauty and economy. However, their utilization has not been optimal in supporting sustainable national economic growth. Therefore, a national agency is needed that specifically manages spices and herbs to increase competitiveness, added value, and their contribution to the Indonesian economy. This study aims to analyze the urgency of establishing the National Spice and Herbal Agency in order to support the vision of Indonesia Emas 2045. The method used is descriptive qualitative research with a case study approach, using secondary data from various relevant sources. The results of the study show that although Indonesia has abundant spice and herbal resources, their management is still sectoral and not yet integrated. Therefore, the establishment of a national agency that specifically handles this sector is a strategic step to create more effective, sustainable governance, and is able to increase the contribution of the spice and herbal sector to the national economy, including in Non-Tax State Revenue (PNBP) and exports.
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 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 | 0.007 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".