Bibliographic record
Abstract
Food safety regulations designed for industrial-scale food producers can create insurmountable challenges when applied to small-scale food producers. These challenges can make for a frustrating environment for food consumers, producers, and regulators, at times leading to tensions between food producers and people working in food safety. The objective of this study was to identify ways to reduce these tensions and promote intersectoral collaboration. We used concept mapping, a structured, participatory, mixed-method approach, to solicit ideas and synthesize input from those working in food safety and food security. We sent invitations to 96 individuals working in food safety or food security, and 50 completed the online concept mapping. Twenty-three participated in categorizing and ranking all the resulting statements. The findings were 'mapped' into six clusters: (1) communicating, (2) understanding intent, (3) educating, (4) understanding risk and regulation, (5) recognizing scale, and (6) enhancing partnerships. We further reduced these six clusters into three categories: "relationships," "education," and "context." Although there are no quick or easy ways to ease tensions between those working in food safety and food security, we suggest four practical ways to ease tensions to ensure safe and accessible food: (1) a collaborative group at a high regulatory level that shares authority is needed; (2) building relationships across disciplines should be considered as part of public health work; (3) regulatory documents should be written in plain language; and (4) food safety regulations should account for differences in scale of production with supportive resourcing. See the press release for this article.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".