Food security under compound shocks: Can Lebanon produce its own Mediterranean food basket?
Bibliographic record
Abstract
As Lebanon faces compound challenges, a looming food security crisis is rapidly approaching, much of which could be attributed to the lack of long-term planning for sustainability in its agricultural sector. The disconnect between decision-makers within the agricultural sector, and other interconnected sectors is exacerbated by the lack of integrative national platforms and methodologies for quantifying the trade-offs associated with possible interventions. This study aims to: (1) identify and quantify the critical interconnections between water, energy, nutrition, and food systems in Lebanon; (2) develop a framework to quantify the trade-offs associated with adopting interventions within current water, energy, and agriculture portfolios and practices; (3) evaluate producers' perceptions toward their willingness to implement proposed changes in crop production, renewable energy, and water reuse. Findings show that investing in locally producing Lebanon's needs of broad beans, lentils, chickpeas, and peas, results in cost savings, increased nutritional value in the locally produced basket, and reduced reliance on foreign markets. In turn, this comes at additional water, energy, land and carbon footprints which needs to be accounted for. Given the uncertainty of future currency conversion rates, it becomes more critical to identify a strategic food basket that could be produced locally to reduce reliance on imports. Conclusions from this study can play a role in informing policymaking and planning in Lebanon, which could be adapted and replicated in other countries in the MENA Region.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".