“These measures make no sense for our farming system”: Biosecurity challenges and adaptations in French small-scale poultry farms selling directly to consumers
Bibliographic record
Abstract
Over the past decade, outbreaks of Highly Pathogenic Avian Influenza (HPAI) have significantly impacted the global poultry industry, resulting in the implementation of biosecurity regulations. Small-scale poultry farmers selling directly to consumers have faced difficulties in implementing these measures due to the specific features of their farms. We examined the social, technical and regulatory rules which drive the production of small-scale poultry farms selling directly to consumers, and then analyzed the barriers and opportunities for implementing biosecurity measures. This study analyzed qualitative data from 36 semi-structured interviews conducted in two French poultry production areas between January and October 2023. The interviews were recorded and transcribed, then thematically analyzed to identify recurring themes. Our results show that small-scale poultry farmers share strong values rooted in their autonomy over decision-making and traditional farming practices that are based on free-range livestock farming, high-value products, and local know-how. Despite widely diverse settings, farmers in this production sector demonstrate a strong sense of professional identity. Our results also show that recurrent HPAI epidemics and associated regulations have disrupted small-scale farming systems. Verbatims revealed tensions between biosecurity guidelines and several aspects of small-scale production, including farming practices (such as visitors' access to farms), values (free-range farming), workload, and financial sustainability. To move beyond these biosecurity tensions, some initiatives have been developed, such as personalized training and advice for small-scale farmers. However, these solutions only reach a small proportion of producers. This suggest that biosecurity compliance on small-scale farms cannot be achieved without simultaneously addressing social, technical, and regulatory issues.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".