A Proportional Hazard Model to Figure out Asian Countries’ Food Insecurity
Bibliographic record
Abstract
A statistical approach is employed to examine the affects of covariates on food insecurity among Asian countries in the period of 40 years since 1961. We conduct a statistical technique based on information aspects of each country on human resources, environment and sustainability, land use and land resource, agricultural resource and capacity, water and sanitation, and macroeconomic indicators. We found that 22 of 32 (65.72%) countries experience insecurity food condition. The remaining are censored observations (34.38%). Stepwise Cox’s regression model is used to select among the 24 independent covariates that are deemed to be significant contribution to the model. Based on the adopted model, at each time point, the West Asian region are found to be more likely to have insecurity food condition compared to those countries in the other regions. Furthermore, the occurrences of food insecurity for East Asia countries are more likely than for those in the other region. Meanwhile, it can also be seen that countries in Lower-middle income group are more likely to reach insecurity food condition than those in the other group. The analysis also shows that the high income countries have high risk of exposure to insecurity food condition.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.015 | 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".