Suppliers’ Perspectives on Cage-Free Eggs in China
Bibliographic record
Abstract
Successful promotion of cage-free eggs supports a housing system offering potential for improved hen welfare. As the world's largest egg producer and consumer, China offers much potential for welfare improvements. We examined 10 Chinese companies supplying cage-free eggs (four using indoor systems, six with outdoor access) to understand their strategies to promote cage-free eggs to businesses and consumers. We purposively sampled 12 employees from these companies familiar with production or sales. We conducted two-three semi-structured interviews per participant, collected public online documents (including online shops and social media content), and recorded field notes. We analyzed the data using template analysis to generate key results. Participants reported buyers being unfamiliar with 'animal welfare' and 'cage-free', but familiar with concepts associated with 'free-range'. Participants considered three attributes when promoting cage-free eggs: price (engaging buyers who were willing to pay more), experiential attributes (e.g., taste, accommodating buyer preferences), and non-sensory credence attributes (e.g., cage-free production, improving buyers' understanding and trust). Our results are not generalizable, though they may be transferable to similar contexts. Understanding how companies promoted cage-free eggs to buyers may help inform promotion of other animal products with welfare attributes. Simultaneous efforts are needed to ensure actual welfare improvements on farms.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".