Upgrading from One-way Informing to Two-way Audience-oriented Health Communication: CFSA Initiations for World Food Safety Day
Bibliographic record
Abstract
Food safety is vital to the development of human society.On December 20, 2018, the United Nations General Assembly adopted resolution 73/250 proclaiming a World Food Safety Day.As of 2019, every June 7 is a time to increase social awareness of food safety and to encourage actions for good health promotion.The Third World Food Safety Day on June 7, 2021 aims to draw attention and inspire action to help prevent, detect, and manage foodborne risks, contributing to food security, human health, economic prosperity, agriculture, market access, tourism, and sustainable development (1).Food safety risk communication shifts from the traditional approach of one-way sender-oriented to a two-way audience-oriented communication approach.International organizations have achieved consensus that recent advancement of technologies and institutions fundamentally impact how the public perceive, communicate, and react to food safety risk issues.It is crucial to conduct audience analysis to gain a comprehensive understanding of risk perception and communication.For instance, the European Food Safety Authority (EFSA) recommended an audience analysis approach for food-related risk communication practices.Data-driven insights are encouraged by EFSA's Social Science Roadmap (2019-2021) (2).It is key to identify and segment audiences, to measure understanding of public information, and to tailor communication methods.The importance of producing and delivering public information through partnership approach and social media integration are also emphasized (3).
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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 | 0.040 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.010 | 0.006 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.008 | 0.015 |
| Insufficient payload (model declined to judge) | 0.031 | 0.006 |
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".