“Dear Dairy, It’s Not Me, It’s You”: Australian Public Attitudes to Dairy Expressed Through Love and Breakup Letters
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
Abstract Understanding evolving public views on food production is vital to ensure agricultural industries remain socially sustainable. To explore public attitudes to the dairy industry, a convenience sample of Australian citizens were asked to write their choice of a ‘love letter’ or ‘breakup letter’ to dairy. The present study provides results from the 19 letters submitted. Participants varied in age, gender identity, income and frequency of consumption of dairy products. The letters were on average 144 words long (range: 48–285), and were categorized into 8 love letters, 6 break-up letters, and 5 ‘distance’ letters that conveyed a conflicted stance. We undertook inductive thematic analysis of all letters, identifying three main themes: (1) personal relationship with dairy; (2) views about dairy as an industry; and (3) views on dairy products. Support for dairy was mainly communicated through participants’ love of dairy products, whilst opposition to dairy largely centered on participants’ ethical concerns about farming practices. Some participants were conflicted in their relationship with dairy, struggling to balance their love of the products and their concerns about farming practices. In contrast, participants who conveyed that they had ‘broken up’ with the dairy industry described an unfailing commitment to their decision. Our findings demonstrate the key role of people’s core values in their relationship with dairy. Efforts to identify and address areas of concern that lead to values misalignment with the public may aid in maintaining the social sustainability of the dairy industry into the future.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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