Harmonization of legislation and regulations to achieve food safety: <scp>US</scp> and Canada perspective
Bibliographic record
Abstract
Trade in food and food ingredients among the nations of the world is rapidly expanding and, with this expansion, new supply chain partners, from globally disparate geographic regions, are being enrolled. Food and food ingredients are progressively sourced more from lesser developed nations. Food safety incidents in the USA and Canada show a high unfavorable correlation between illness outbreaks and imported foods. In the USA, for example, foodborne disease outbreaks caused by imported food appeared to rise in 2009 and 2010, and nearly half of the outbreaks, associated with imported food, implicated foods imported from areas which previously had not been associated with outbreaks. Projecting supply chains into new geographical regions raises serious questions about the capacity of the new supply chain partners to provide the requisite regulatory framework and sufficiently robust public health measures for ensuring the safety of the foods and foodstuffs offered for international trade. The laws, regulation and legislation among the many nations participating in the global food trade are, at best, inconsistent. These inconsistencies frequently give rise to trade disputes and cause large quantities of food to be at risk of destruction on the often dubious pretext that they are not safe. Food safety is often viewed through a political or normative lens. Often as not, this lens has been wrought absent scientific precision. Harmonization of food safety legislation around sound scientific principles, as advocated by the US Food Safety Modernization Act (FSMA), would ultimately promote trade and likely provide for incremental improvement in public health. Among the priority roles of most national governments are the advancement of commerce and trade, preservation of public health and ensuring domestic tranquility. Achieving these priorities is fundamental to creating and preserving the wealth of nations. Countries such as the Netherlands, Canada, Germany, Japan and the USA, for example, have very stable governments, are leaders in trade and commerce and enjoy high standards of public health. It is not by accident or coincidence that these nations are also among the world's wealthiest. Attainment of national priorities, especially those related to promoting trade in foodstuffs and also in preserving public health (food safety), would benefit greatly from international efforts in harmonizing food safety regulations and legislation.
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 | 0.020 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.015 | 0.017 |
| Scholarly communication | 0.019 | 0.006 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.020 | 0.014 |
| Insufficient payload (model declined to judge) | 0.010 | 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".