An Econometric Analysis of Food Security Determinants in Malaysia: A Vector Error Correction Model Approach (VECM)
Bibliographic record
Abstract
Food security issue is getting more attention by world today. Although, Malaysia is a middle income country able to produce her own food, but there is still lack of food supply for domestic needs. This paper thus analyse the factors that affect the food security model in Malaysia during the period of 1982-2011. The analysis in this paper include food production index as food security proxy while the other variables include food prices, Malaysian population, foreign workers and CO2 emission as important determinants of food security. The assessment of the impact of these factors is achieved using the Vector Error Correction Model approach (VECM). The series on the food prices, Malaysian population, foreign workers, CO2 emission and palm-based biodiesel production are co-integrated. While in the short run only foreign worker is an important determinant of food security. Hence, the results of error correction term (Ect) from VECM shows that there is a long run causality between dependent variables and explanatory variables. This model is useful quantitative tool to assess food security especially to determine specific variables that explain the highest effect to food security at the national level.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".