Views of American animal and dairy science students on the future of dairy farms and public expectations for dairy cattle care: A focus group study
Bibliographic record
Abstract
Students completing advanced degrees in dairy or animal science may go on to have a major impact on the food animal agriculture industries. The aim of this study was to better understand student views of the future of dairying, including changes in practices affecting animal care on farms as well as perceived public perceptions. We conducted 6 focus group sessions with undergraduate students enrolled in the 2019 US Dairy Education and Training Consortium held in Clovis, New Mexico, and used explorative key word analysis of written notes and thematic analysis of the semi-structured discussions. Some "must-haves" of future animal care on dairy farms included increased use of technology, group housing of calves, and adequate facilities, including enrichment. Students also discussed their views of public expectations regarding animal care on dairy farms, and measures that they felt must be put into place to address these expectations in the coming years. Although the influence of the public was highlighted by the students, they were not always certain what specific values the public holds and doubted the feasibility and practicality of some expectations, such as providing pasture access or keeping the calf and cow together. They further demonstrated uncertainty about how best to align the directions of the industry with public expectations. Although they felt that public education could be used to demonstrate the legitimacy of dairy practices, they also believed that the industry should strive to find compromises and work toward meeting public expectations. Deciding what animal welfare considerations (e.g., naturalness, affective states, or animal health) were most relevant was a challenge for the students, perhaps reflecting diverging messages received during their own education.
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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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| 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".