Design to Improve Sustainable Employee Welfare Using the R-Studio Application: The Case of the Tofu Agro-Industry in Central Lombok, Indonesia
Bibliographic record
Abstract
This study assesses the welfare of industrialists and employees of the tofu agro-industry in Central Lombok, Indonesia.The analysis tool used was the producer's surplus compared to the provincial minimum wage.To achieve this goal, data was collected using triangulation techniques, which combine observation, survey, and in-depth interview methods.The respondents are industrialists and employees of the tofu agro-industry in each village, specifically 26 business units in Puyung Village and four household-scale business units in Aikmual Village.The number of business unit samples in each village is determined proportionally to the number of business units in each village.The results of the study show that the R-Studio application can be used to calculate the producer's surplus with precision results.The agro-industrial entrepreneurs in Puyung Village are classified as very prosperous, while the welfare entrepreneurs of the tofu agro-industry in Aikmual Village is classified as prosperous, and employees in the two villages are at a prosperous level.Sustainable employee well-being can be achieved through efforts to increase productivity, the selection of product sizes according to market demand, and the use of fuel-efficient technologies.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".