Prioritizing Food Security Indicators in Iran: Application of an Integrated Delphi/AHP Approach
Bibliographic record
Abstract
Background: Monitoring of food insecurity is a critical issue for planners and policymakers in the public and private sectors in developing countries. Due to the multifactorial and multidimensional nature of food security and a lack of clarity concerning the causes, specific signs, and consequences of food insecurity, developing a reliable food security index is the major challenge related to monitoring food security. Objectives: The objective of this study was to identify the most appropriate indicators of food security at the provincial level in Iran through the application of an integrated approach including Delphi (classic Delphi) and analytic hierarchy process (AHP) from March to September 2013. Materials and Methods: The sample included 43 senior-level managers and experts at the national and provincial levels from different fields of related sciences; they were selected purposively as Delphi and AHP panel members based on the experts’ opinions and snowballing. In the first round of Delphi, out of 103 identified indicators, 38 were selected by the experts; the indicators were ranked in the second round. In the AHP study, 25 experts assigned weights in a pairwise comparison of the 20 indicators that had the highest priority based on the Delphi results. Using AHP matrix calculations, this list of indicators was ranked based on priority. Results: Out of 38 indicators identified in Delphi, 8 were related to the availability dimension, 14 were related to the access dimension, and 16 were related to the utilization dimension. Out of 20 indicators that were ranked in the AHP study, 6 indicators were related to availability, 7 were related to utilization, and 7 were related to access dimensions. However, the indicators related to availability had an overall higher rank compared to indicators related to access or utilization. Conclusions: This study identified and ranked 20 indicators as the most appropriate indicators of food security measurements at the provisional 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 | 0.054 | 0.036 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.010 | 0.006 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| 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".