‘Space, pasture, water, grain (and) love’: dairy cattle needs according to citizens visiting a dairy farm
Bibliographic record
Abstract
Citizen engagement on issues around farm animal welfare is important for the livestock industries to sustain their social license to produce. One barrier to this engagement is that non-farming citizens are sometimes perceived as ignorant of farming practices and their views are dismissed. Despite recent interest in understanding citizen views on farm animal welfare in Europe, little is known about North Americans’ views. We surveyed the views of 67 citizens before and after a self-guided tour of a working 500-head dairy farm in Agassiz, British Columbia. The mixed-methods survey used quantitative and qualitative questions designed to explore the range of perceptions and concerns about dairy cattle welfare. Of those who responded, 60% were female, 96% consumed dairy products and 93% reported themselves as either somewhat or not knowledgeable about dairy farming. Content analysis revealed that, before visiting the farm, participants considered the following elements (in decreasing frequency) as necessary for dairy cattle to have a good life: fresh food and water, pasture access, gentle human care, space, shelter, cleanliness, fresh air and sunshine, social companions, absence of stress, health, and safety from predators. In general, the farm visit appeared to mitigate some concerns (e.g. provision of adequate food and water, gentle human care) while reinforcing others (e.g. lack of pasture and outdoor access, early cow-calf separation). These results illustrate a multi-dimensional conception of animal welfare that links with values of other livestock industry stakeholders. The results also indicate that efforts to educate citizens may address some concerns but are also likely to create awareness of other issues relating to the welfare of farm animals.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".