Identification and prioritization of food insecurity and vulnerability indices in iran.
Bibliographic record
Abstract
BACKGROUND: Food security is a multi-dimensional phenomenon. The objective of this study was to identify and prioritize major indices for determining food insecurity in Iran. METHODS: Descriptive study using the Delphi method was conducted through an email-delivered questionnaire. Forty-three senior experts at national or provincial level were selected based on their work experience and educational background through study panel consultation and snowballing from Tehran and other cities of Iran. During two rounds of Delphi, participants were asked to identify priority indicators for food security at provincial level in Iran. RESULTS: Sixty five percent of Delphi panel participated in the first round and eighty-nine percent of them participated in the second round of Delphi. Initially, 243 indices were identified through review of literature; after excluding indictors, which was not available or measurable at provincial level in Iran, 103 indictors remained. The results of study showed that experts identified "percentage of individuals receiving less than 70% of daily energy requirement" with a median score of 90, as the most influential index for determining food insecurity. "Food expenses as a proportion of the overall expenses of the family", "per capita of dietary energy supply", and "provision of micro-nutrient supply requirement per capita" with median of 80 were in the second rank of food security priority indicators. CONCLUSION: Out of 243 identified indicators for food security, 38 indicators were selected as the most priority indicators for food security at provincial level in Iran.
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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 | 0.008 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".