Exploring the legitimacy of industry-led farm animal welfare governance using examples of Canadian and United States dairy standards
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The governance of farm animal welfare is led, in certain countries and sectors, by industry organisations. The aim of this study was to analyse the legitimacy of industry-led farm animal welfare governance focusing on two examples: the Code of Practice for the Care and Handling of Dairy Cattle and the Animal Care module of the proAction programme in Canada, and the Animal Care module of the Farmers Assuring Responsible Management (FARM) programme in the United States (US). Both are dairy cattle welfare governance programmes led by industry actors who create the standards and audit farms for compliance. We described the normative legitimacy of these systems, based on an input, throughput, and output framework, by performing a document analysis on publicly available information from these organisations' websites and found that the legitimacy of both systems was enhanced by their commitment to science, the presence of accountability systems to enforce standards, and wide participation by dairy farms. The Canadian system featured more balanced representation, and their standard development process uses a consensus-based model, which bolsters legitimacy compared to the US system. However, the US system was more transparent regarding audit outcomes than the Canadian system. Both systems face challenges to their legitimacy due to heavy industry representation and limited transparency as to how public feedback is addressed in the standards. These Canadian and US dairy industry standards illustrate strengths and weakness of industry-led farm animal welfare governance.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it