Social and Economic Aspects of the Transmission of Pathogenic Bacteria between Wildlife and Food Animals: A Thematic Analysis of Published Research Knowledge
Bibliographic record
Abstract
Wildlife is a known reservoir of pathogenic bacteria, including Mycobacterium bovis and Brucella spp. Transmission of these pathogens between wildlife and food animals can lead to damaging impacts on the agri-food industry and public health. Several international case studies have highlighted the complex and cross-sectoral challenges involved in preventing and managing these potential transmission risks. The objective of our study was to develop a better understanding of the socio-economic aspects of the transmission of pathogenic bacteria between wildlife and food animals to support more effective and sustainable risk mitigation strategies. We conducted qualitative thematic analysis on a purposive sample of 30/141 articles identified in a complementary scoping review of the literature in this area and identified two key themes. The first related to the framing of this issue as a 'wicked problem' that depends on a complex interaction of social factors and risk perceptions, governance and public policy, and economic implications. The second theme consisted of promising approaches and strategies to prevent and mitigate the potential risks from transmission of pathogenic bacteria between wildlife and food animals. These included participatory, collaborative and multidisciplinary decision-making approaches and the proactive incorporation of credible scientific evidence and local contextual factors into solutions. The integration of these approaches to address 'wicked problems' in this field may assist stakeholders and decision-makers in improving the acceptability and sustainability of future strategies to reduce the transmission of pathogenic bacteria between wildlife and food animals.
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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 | 0.031 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.023 | 0.029 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".