Responding to globalised food‐borne disease: risk assessment as post‐normal science
Bibliographic record
Abstract
Since the 1960s, global trade in food and feed has increased rapidly, and the number of countries at least partially reliant on this trade has sprouted into complex International Agrifood Trade Networks (IATN). IATNs have obscured the already-labyrinthine causal webs of food-borne diseases, and the usual methods for demonstrating causal links between IATNs and food-borne diseases yield results that are, at best, inconclusive. At the same time, responses are being offered which will, if implemented, likely to have unintended negative consequences. In this context, risk analysis (RA) is being used in situations for which it was not designed, in which facts are uncertain, values are in dispute and assessments are embedded in contested power arrangements, with heterogeneous consequences for diverse stakeholders around the world. To characterise and manage the most serious unintended food-borne disease consequences of globalisation, the most effective way forward will require reframing of RA as a post-normal science.
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 | 0.132 | 0.109 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.003 | 0.070 |
| Scholarly communication | 0.024 | 0.029 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.009 | 0.017 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".