Policy discourse on AMR in food-producing animals: examining framing and language for effective communication
Bibliographic record
Abstract
Background: Indiscriminate use of antimicrobials in food-producing animals is a critical driver of antimicrobial resistance (AMR). However, pushback from stakeholders on policies and regulations on antimicrobial use in food-producing animals remains. One important strategy to promote behavioural change is effective communication. Framing, or how issues are constructed to relate to specific interests, is a mechanism to guide sentiment, including of political stakeholders and end-users. Methods: Through a sector-specific approach, we used a combination of inductive and deductive coding to quantitatively determine how risk and rationale for action were framed within portions of policy documents and reports from international organizations focused on food-producing animals and AMR. We also qualitatively examined the frames and language used within the documents, to identify specific narratives used, as well as gaps and opportunities to improve communication for end-user support and political legitimacy. Results: We found that while similar motivational frames are used throughout, they were distributed differently and utilized different narratives. The most frequently used motivational frame, on average, was 'Human Health' (20.9% of all frames used) with 'Animal Health and Welfare' and 'Food Production and Security' second and third, respectively (18.2% and 14.5%). Self-interest frames specific to the farmer or farm worker were rarely used. Conclusions: Specific recommendations include increasing self-interest frames, ensuring accessibility of messaging and considering underlying assumptions. Overall, our findings can improve framing and language to improve resonance on policies surrounding antimicrobial use in food-producing animals. This work provides a framework to systematically analyse framing in documents to compare different sectors or regions.
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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 | 0.073 | 0.100 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.006 | 0.016 |
| Scholarly communication | 0.010 | 0.013 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| 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".