Examining the Effect of Agribusiness Actors' Performance on the Performance of Arabica Coffee Agribusiness Subsystem in North Toraja Regency
Bibliographic record
Abstract
The performance of agribusiness actors is measured to evaluate the efficiency of the Arabica coffee agribusiness system.This study aims to analyze the effect of the performance of agribusiness actors (Department of Agriculture, Companies, Coffeeshops, Micro, Small, and Medium Enterprises, Agromechanical Industry, Agrochemical Industry, Cooperatives, Collecting Traders, Agricultural Extension, Farmer Groups, Farmers) on the performance of subsystems (Upstream, Farming, Downstream, Marketing, and Supporting) of Arabica coffee agribusiness.This study used a descriptive qualitative and quantitative approach.The data analysis technique uses Smart PLS 3.0 software, Structural Equation Model -Partial Least Square (SEM-PLS) analysis.The analysis was carried out partially because indicators intersected or were interrelated between actors and agribusiness subsystems.The results showed that there were variables that had a significant and positive effect, including the performance of the Agriculture Office on the Upstream and Supporting Subsystems; Companies on farming, downstream, and marketing subsystems; Coffeeshops on downstream and marketing; MSMEs on marketing; Cooperatives on support; Farmers on upstream, agriculture, and marketing.In addition, some variables have an insignificant and negative effect, namely the performance of the company to the supporting subsystem, Agromechanics and agrochemicals to upstream; Cooperatives to marketing, Collecting Traders to marketing, Extension to supporting; Farmer groups to farming and supporting subsystems.The research conclusion shows that 11 variables have a significant and positive effect, while the remaining nine have a negative and insignificant impact.Arabica coffee agribusiness performance can be improved through policy support that favors the country's mainstay commodities such as arabica coffee.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".