263 Public perspectives of calf handling: A survey of Canadians
Bibliographic record
Abstract
Abstract In North American beef production handling and restraint of young calves is integral to animal welfare and management practices. This study used a mixed method approach to gather public perceptions of three handling and restraint methods common on beef operations in western Canada (RW - Roping and Wrestling, NF – roping and NordForks, and TT – Tilt Table). Canadian participants (n = 551) recruited by CloudResearch to represent the national demographics participated in an online survey that included videos of each handling method to ascertain preferences and acceptability. Prior to watching the videos, participants were given industry information about handling and restraint or generic information regarding hay as a control information statement within the topic of agriculture. The survey also collected information about knowledge of the beef industry, animal welfare, and empathy toward animals. The reasons for preferences were described as presence of a perceived positive attribute and absence of a perceived negative for most preferred methods, and inversely when explaining least preferred method. The findings focused on calf’s experience, perception of handler actions, and pragmatic balancing of handler needs and a good life for the calf. Methods were rated as more acceptable for participants that ate meat consistently, knew more about the beef industry, and, to a lesser extent, if the individual had a lower animal empathy score (Table 1). Acceptability was not affected by providing information about the practices; however, information about handling and restraint did elicit more pragmatic reasoning. Most participants preferred TT over NF and RW (P < 0.001) and found TT more acceptable as well (P < 0.001). The TT was the most preferred method due to calf experience and human handling, notably the absence of dragging a calf, which was predominant in why participants selected NF or RW as their least preferred method. Consistency of themes highlights that the Canadian public has a fundamental expectation of value for the quality of life of the calf, humane handling, and pragmatism which aligns with beef sustainability initiatives and represent points of connection for building trust between industry and the public.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".