An Economic Analysis of Meat Demand in Indonesia
Bibliographic record
Abstract
Meat is one of animal protein source which contains with good nutrition for human life. Consumption of meat is low than consumption of other animal protein sources because of its availability and expensive price. The objective of this study is to analyze of meat consumption pattern and the factors affecting it. The method used was descriptive and econometrics analysis. Specification of model was Quadratic of Almost Ideal Demand System (QUAIDS) and modified with size of household and wife education variable. Data used was the data of national socio-economic survey 2005, 2008, 2011, and 2014. Results of the study found that variation in the amount of meat consumption dependent on income level, price, education of wife, and size of household, also is closely related to the household area. Increasing consumption of meat as a source of animal protein was increase income, price stability, support the availability of meat at the household level, especially for low income also escorted with information about the important role of nutrients.
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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 | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| 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".