Les politiques de qualité sanitaire des aliments en droit international, Union Européenne, USA, Canada, et France.Quelles organisations sont envisageables pour améliorer la sécurité sanitaire des aliments au Liban ?
Bibliographic record
Abstract
Following the establishment of the Lebanese food safety authority (LFSA) in the food safety draft law, this study aims to find the appropriate food safety governance system in Lebanon in accordance with the risk analysis and the clauses of the World Trade Organization (WTO) Sanitary and Phytosanitary (SPS) Agreement. It describes the regulatory and institutional food safety governance system developed in USA, EU, Canada and France to profit from their previous experience in this field. The findings indicate that Lebanon showed compliance with the clauses of the WTO but it will not be for the interest of Lebanon to join the WTO before it strengthens its infrastructure capacity at the institutional and stakeholders level through harmonization of the risk assessment and risk management at the institutional level and at all stages of the food chain. Lebanon should rely on bilateral and multilateral agreements by carrying out equivalence that fits his socio-economic situation based on risk assessment. As it was shown in the studied countries, power given to the food safety authority depends on the willing of the country to adopt the political democratic approach or political scientific approach. Risk management and risk assessment were not always functionally and institutionally separated. Lebanon within its political and socio-economical situation will meet constraints if it relies only on scientific justification, it should adopt the political democratic approach and ensure collaboration among risk assessment agencies and risk management at different levels.The studied countries showed different food safety systems. The results of the SWOT analysis on possible scenarios taken from models used in the studied countries have shown that the models taken in North America are interesting in their proximity of work between the risk assessors and the risk managers. In order for Canada to avoid the conflict of work between the FDA and the USDA, it has created a single inspection agency (CFIA). However, the system used in the United States and Canada cannot be applied in its proximity of work between risk assessment and risk management in Lebanon, as the risk management in Lebanon belongs to different Ministries . The system used in France is more suitable for the case of Lebanon, but this system cannot solve the overlap of work between concerned ministries and does not place the risk assessment in proximity with the risk management.The results of the SWOT analysis showed the need to create an inspection and control agency. In this case the risk assessment, developed along with the development of the food technology, will be in proximity with the inspection, monitoring and control tasks of the ministries while avoiding any conflict of political decision with them. The analysis of a particular Lebanese dairy case showed that there is a lack of data transmission between the three ministries (MOH- MOA- MOET) and the risk assessment agency in the scenario close to the North America model. Also, it has shown an overlap of work and a lack of data transmission for the monitoring and control process between the ministries in the scenario close to the French model. This situation should be solved by adopting the proposed model which creates a single agency for inspection, monitoring and control process close to the assessment agency.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".