The Importance of Good Stockmanship and Its Benefits to Animals
Bibliographic record
Abstract
This chapter describes the role of good stockmanship on the improvement of animal welfare, the detrimental effects of fear on productivity, the importance of positive attitudes towards animals for improving both welfare and productivity; how to train stockpeople to have more positive attitudes and how to remedy barriers to improving stockmanship. Good stockmanship will improve both animal welfare and productivity. Dairy cows, pigs, and other animals that are fearful of people will have lower weight gain, lower milk production, and poorer reproductive productivity. Farms where animals are willing to approach people may be more productive. This chapter reviews many studies that clearly show the relationship between aversive (bad) treatment and lower production. Animals that have been hit or shocked may become fearful of all people. Stockpeople who have a positive attitude towards animals often have animals with increased productivity. Studies also show how training can be used to improve the attitudes of stockpeople. The animal's relationship with the stockperson is not the only factor that determines productivity. Farm cleanliness and a stockperson's attention to good management practices is also extremely important. To help stockpeople maintain a positive attitude, they must not be worked to the point of becoming exhausted. Managers must recognize that a good stockperson is a highly skilled professional who should receive recognition for their work and adequate pay.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 0.004 |
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".