Communication of food-related risks
Bibliographic record
Abstract
Assessing the scientific risks of agri-food technologies must be coupled with appropriate, research-based risk management and communication activities, in order to provide consumers, the media, and others with a balanced, science-based assessment of both the potential benefits and risks of a particular technology. Those responsible for risk management must openly communicate their activities to reduce levels of risk. Key Words: risk communication; risk perception; food safety. The ability to effectively communicate about food safety—and indeed, any perceived or technologically induced risk—is now recognized as an integral component of an integrative risk management strategy (Powell, 2000). The Canadian (and international) food sectors are facing a crisis of confidence, as awareness of food related risks (such as, E. coli O157:H7, bovine spongiform encephalopathy (BSE), and genetically engineered foods) has been elevated to the national stage. The use of chemicals in agriculture, along with other food-related technologies, management techniques, and ethical concerns (such as animal welfare) are being intensely scrutinized, and continue to raise questions. But the ability to apply science-based solutions to such challenges is intricately dependent on issues of public perception, the regulatory environment, fairness,
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 | 0.010 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.016 | 0.003 |
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".