“Dear Dairy, It’s Not Me, It’s You”: Australian Public Attitudes to Dairy Expressed Through Love and Breakup Letters
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.011 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.008 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| 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".