Implementation of Participatory Rural Appraisal (PRA) in Empowering Gaplek SMEs Using Partial Least Square (PLS) Analysis
Bibliographic record
Abstract
The improvement of tapioca production system is one of the solutions to improve the economy of rural communities through the development of cassava inventory as an inexpensive tapioca raw material that can be obtained throughout the year, using appropriate management and strategies. This study aims to determine the order of priority criteria that are influential in determining the steps to improve production system performance. The method used is analysis PLS using six criteria, namely local potential, empowerment capital, social community, empowerment process, community empowerment, the ability of actors. The empirical results of the study show that the highest R-square value is the empowerment process at 61.0 percent. This value indicates that local potential, empowerment capital, social community, and the ability of the actors are able to contribute to the explanation of the empowerment process by 61.1 percent, while 38.9 percent is influenced by other variables. The R-square value on the community empowerment variable is lower than the R-square value for the empowerment process, which is only 42.4 percent. This shows that the variables of local potential, empowerment capital, social community, the ability of empowerment actors, and the empowerment process do not adequately explain community empowerment. From the results of the exploration of the overall influence factors with the PLS analysis mentioned above, it is used to invite people to understand themselves and formulate ideas for developing Gaplek SMEs in the form of a vision with a strategy for achieving them. Based on the empirical results of research through testing the accepted hypothesis with the intervening variable having a significant positive relationship, appropriate mentoring is carried out to focus on the mentoring process with the Participatory Rural Appraisal approach.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".