Food Safety: FDA Can Better Oversee Food Imports by Assessing and Leveraging Other Countries' Oversight Resources
Bibliographic record
Abstract
A letter report issued by the Government Accountability Office with an abstract that begins "We identified five major actions the Food and Drug Administration (FDA) is to complete under the FDA Food Safety Modernization Act (FSMA) to establish a reliable system that uses third-party audits conducted by foreign governments or other third parties to help ensure food safety. FDA officials and others report that each of these actions presents challenges that must be addressed. First, FDA is to develop new preventive controls and related guidance for all of the foods under its jurisdiction--such as produce, milk, cheese, spices, soft drinks, and processed foods--and will need to develop appropriate training, particularly for foreign producers and processors, which poses a challenge because FDA is responsible for a variety of food industries. Second, FDA is to establish a voluntary user fee program for importers that encourages the use of third-party certifications, and it faces a challenge in developing a program that encourages importers to participate. Third, FDA has to develop a system for recognizing accreditation bodies that can accredit third parties to certify foreign food facilities and is likely to face a challenge in addressing foreign governments' concerns about being evaluated by an entity other than FDA. Fourth, FDA is to develop model standards for accreditation bodies to use in evaluating and accrediting third parties and faces challenges in, among other things, determining third-party auditors' competency and deciding on how to avoid potential conflicts of interest. Fifth, FDA is to oversee the third-party accreditation system, including periodically evaluating accreditation bodies and third parties, and faces a challenge in deciding the level of oversight it will provide to the multiple parties involved in third-party certification."
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 | 0.051 | 0.091 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.013 | 0.013 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.018 | 0.008 |
| Insufficient payload (model declined to judge) | 0.033 | 0.015 |
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".