Opportunities and Challenges for Lebanese Horticultural Producers Linked to Corporate Buyers
Bibliographic record
Abstract
This paper aims to analyze procurement decisions and contractual arrangements in the horticultural supply chain and evaluate opportunities for and challenges of horticultural producers linked to supermarkets and corporate restaurants in Lebanon. Accordingly, in-depth, semi-structured interviews were conducted with key horticultural supply chain actors in Lebanon. The study finds that corporate restaurants offer more opportunities for large horticultural producers and suppliers than supermarkets. Yet, corporate restaurants have more stringent quality requirements, as demonstrated by food safety certifications, and their contractual relationships are binding, symbiotic, and formal. Supermarkets source most of their products from wholesale markets and have opportunistic, non-binding relationships with their suppliers. In sum, the nature of the business relationships between horticultural producers and suppliers and corporate buyers depends on the ability of the producers to meet the quality requirements of the latter. Although corporate buyers have shown some interest in the local produce, they are yet to invest in local supplier development initiatives to enhance the capabilities of producers. Instead, corporate buyers resort to imports when the local producers fail to meet the quality standards or required volumes. The study suggests several alternative routes to enhance the market position of horticultural producers and suppliers in Lebanon.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".