Economic Analysis of Food Security in Peshawar, Pakistan
Bibliographic record
Abstract
To investigate and explore the condition of food security in District Peshawar, Khyber Pakhtunkhwa, 300 citizens were interviewed. The econometric tool of Pearson’s Product Moment Correlation Coefficient analysis was used and applied for analysis and estimating the data collected. It was concluded from the analysis that food security shows strong and negative relationships with the rise in population growth, rise in biofuel production, rise in poverty and rise in social unrest. Food insecurity is a major issue in KP that must be solved as soon as possible. Poor people are suffering the most and are unable to buy basic food items due to high prices. Food demand is increasing day by day because of larger population, thus resulting in an inflation of food prices. Effective measures are needed to control and reduce the growing rate of population. To eradicate food insecurity, agricultural institutions must be intensified and strengthened, infrastructure and storage facilities must be enhanced and developed, investment and latest machinery & technology are required to be inserted into an inactive agricultural sector.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".