Accounting Training Module Development to Boost Agriculture Financial Literacy on Palm Farmers
Bibliographic record
Abstract
This research aims to develop agriculture accounting training module in order to increase palm oil farmer financial literacy, in this case farmers in Donomulyo, Malang Regency, Indonesia. The method utilized in model development is Design Based Research using the following progression: problem identification, explanation of goals, design and development of artifacts, artifact testing, evaluation on artifact testing, and communication of artifact testing result. Examination was conducted on 25 palm oil farmers, through individual learning on agriculture accounting training to increase financial literacy. Module effectivity was determined should 50% of community members apply separate accounting records for agriculture and household respectively. Module development result has been validated and revised by economy lesson plan experts, education media experts, and agriculture accounting experts. Module composition consists of Chapter 1 (An Introduction to Agriculture Accounting), Chapter 2 (Accounting Basic Procedure), Chapter 3 (Agriculture Break Event Point), Chapter 4 (Agriculture Opportunity Cost Calculation), Chapter 5 (Palm Oil Farmer Household Financial Management). Graphic design provides colorful layout to increase learners’ interest and motivation to learn module content. 76% of the total number of participating farmers have utilized modules and implemented accounting in daily life.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".