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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.015 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.008 |
| 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".