Managing Antimicrobial Resistance in Food Production: Conflicts of interest and politics in the development of public health policy
Bibliographic record
Abstract
Antimicrobial resistance is a growing public health concern and is associated with the over- or inappropriate use of antimicrobials in both humans and agriculture. While there has been recognition of this problem on the part of agricultural and public health authorities, there has nonetheless been significant difficulty in translating policy recommendations into practical guidelines. In this paper, we examine the process of public health policy development in Quebec agriculture, with a focus on the case of pork production and the role of food animal veterinarians in policy making.We argue that a tendency to employ strictly techno-scientific risk analyses of antimicrobial use ignores the fundamental social, economic and political realities of key stakeholders and so limits the applicability of policy recommendations developed by government advisory groups. In particular, we suggest that veterinarians’ personal and professional interests, and their ethical norms of practice, are key factors to both the problem of and the solution to the current over-reliance on antimicrobials in food production.
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 | 0.116 | 0.092 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.015 | 0.042 |
| Scholarly communication | 0.024 | 0.009 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.019 | 0.012 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".