Responsible Stakeholders in Food Risks Communication and Informed Consumers in Surulere Area of Lagos State, Nigeria
Bibliographic record
Abstract
In developing countries, the capacity to handle life-threatening health and nutritional risks is under great threat. Studies have emphasised effective communication of food risks as a measure for motivating behavioural patterns which would correct this imbalance. This study investigates the media used for publicity of food risks, level of awareness and sensitivity of consumers, as well as stakeholders' collaboration for effective prevention and control of food risks. The mixed-method design utilised involved the distribution of 110 copies of questionnaires among consumers within Surulere Local Government in Lagos and interview sessions with representatives of food manufacturing companies, mass media and regulatory bodies. Television, radio, social media, and public messages by the National Agency for Food and Drug Administration and Control (NAFDAC) were prominent media of food risks communication; the activities of the regulators led to increase in consumers' awareness and sensitivity to food risks and their benefits. Manufacturers were found to adhere to standards in food production, storage, and distribution. There was an effective collaboration among stakeholders and leading to food safety, trust and standard maintenance, and quick information provisioning.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| 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".