Bibliographic record
Abstract
The study aimed to investigate operation of oil palm industry based on Green Economy concept. The conceptual framework of the Green Economy for the oil palm industry was established in accordance with Thailand’s context. Information was compiled via literature review on definition, meaning, concept, and theory of the Green Economy, and the oil palm industry, as well as interviews with 15 specialists, experts, scholars, farmers, and officials of relevant agencies such as the Ministry of Agriculture and Cooperatives.The information from the interviews was analyzed by grouping issues and content analysis, and description to obtain the conceptual framework. Then, the framework was assessed by specialists, experts, and officials from the Ministry of Agriculture and Cooperatives with expertise in oil palm, and farmers for implementation.The study findings revealed that the application of the conceptual framework of the Green Economy for the oil palm industry consisted of the Sufficiency Economy Philosophy, Roundtable on Sustainable Palm Oil (RSPO), economic development such as development of organic farming, management of yields and waste, social development such as better quality of life for farmers, higher negotiating power for farmers, equality; and environmental development such as balanced eco system, reduced impact on the environment, and use of environmental-friendly technology.
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 | 0.006 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 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".