Farmer Group Performance of Collective Chili Marketing on Sandy Land Area of Yogyakarta Province Indonesia
Bibliographic record
Abstract
The aim of this research is to examine the relationship between both individual background and performance of collective actions in relation to the different forms of collective marketing. This research measured the two pioneering farmer groups who successfully carried out collective marketing. The percentages of collective marketing are similarly obtained by each group, but the rules for carrying out collective marketing differ. The individual background and performance of collective actions other than collective marketing among members should be considered to describe the different forms of collective marketing. A total of 120 members were interviewed from the two farmer groups that were chosen by stratified land cultivating area and random sampling. Performance of collective action was measured through the attitude toward selling chilies and the effort to find the seeds and labor sources. Next, all data were analyzed by multiple regression analysis. The result indicated that percentage of selling on collective marketing on Bugel is influenced by age and possibility on buying seed collectively through the group, off-farm job and plastic application. However, the difference result is appeared on Garongan farmer group, received remittance, conducted custom help labor and utilizing non-subsidized fertilizer are influences the percentage of selling chili on collective marketing. In Bugel’s farmer group, farmer with stable off-farm income who behave opportunistically in terms of collective marketing tend to hold the power and drive the group performance loosely organized. Garongan’s farmers respect the norms of collective action to achieve purposes that keeping the organization tightly and functioning smoothly.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".