Utilization of Coconut Fiber as a Poor Households Empowerment Base (A Case in Bongomeme District of Gorontalo Regency, Indonesia)
Bibliographic record
Abstract
Poor households have not utilized coconut fiber that is very potential to improve their welfare. This study analyzed the root causes of poor households not yet utilizing the potential of coconut fiber craft as a source of livelihood. The potential of crafts that can be developed from coconut fiber and the strategy of building institutional, commercial business groups of poor households are based on the manufacture of coconut fiber crafts to be competitive and sustainable. The researchers collected data through observation, in-depth interviews, focused group discussion, and literature review. The results show that lack of knowledge is at the root of the leading cause of poor households not utilizing coconut fiber as their livelihood. The other causes are lack of skills, low education, weak access to information, lack of collective awareness, and a false understanding that coconut fiber handicraft products are not sold in the market. Even though the facts show that if processed into handicraft products, coconut fiber can be used by poor households to improve their welfare so that they can be economically empowered. Various strategies can be carried out to build the institutional economic business groups of poor households based on the manufacture of coconut fiber crafts, namely critical awareness, strengthening the capacity of poor households, both through skills training, on the job training, and in-service training. Besides, comparative studies of entrepreneurship can also be carried out, opening up access to information, opening access to micro-business financing, and building networks of poor households to the outside world.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".