Use of Collective Expertise as a Tool to Reinforce Food Safety Management in Africa
Bibliographic record
Abstract
The Erasmus+ project (2017-2020) entitled Societal Challenges and Governance of African Universities: the case of ALIments in Morocco, the Democratic Republic of the Congo and Senegal (DAfrAli) seeks to strengthen the governance capacity of African Higher Education Institutions to mobilize their resources in order to respond to major societal challenges in relation to external stakeholders. A work package consisted of organizing three workshops to use Collective Expertise as a tool for the identification of societal risks, in the area of food safety. These three workshops were conducted in Morocco, in Senegal and in Democratic Republic of Congo. The exercise was performed by country academics with the contribution of the European project partners. Collective Expertise gave results that demonstrated that, with a careful and diversified selection of experts, this methodology can have a deep importance to list the food hazards in a country. The results obtained can induce changes in university curricula, showed the social impacts of food safety, unveiled research needs and training needs for different agents in the food sector and above all the impact in food policy in a country. The collective expertise approach of the determination of hazards also permitted to discuss possible organization models for food risk management in the 3 countries.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".