Social referents for dairy farmers: who dairy farmers consult when making management decisions
Bibliographic record
Abstract
Social referents provide information that aid in farmer decision making. Some social referents, such as veterinarians, have been well studied in the context of dairy farms, but others have not and the role of social referents may change across management practices. This study aimed to understand (1) who farmers consult when making management decisions across different animal care practices and (2) what characteristics of these social referents influence farmer decision making. Secondary thematic analysis was used on two qualitative datasets with dairy farmers from the lower Fraser Valley region of British Columbia, Canada. The two datasets included non-naturalistic data (i.e. interviews, participatory discussion groups) investigating two dairy farm management practices (calf care and transition period management). Analysis revealed four themes: (1) who farmers consult when making management decisions across practices and the role of these social referents, (2) personal and professional characteristics, and the diversity of opinions of social referents, (3) actions of social referents, and (4) the strength of the relationship between the social referent and farmer. Similarities were found across practices regarding the personal and professional characteristics of social referents, even though the role of these referents varied across contexts. Farmers valued diverse opinions and actions that social referents could provide, such as the provision of resources, recommendations, and interpretation of farm data. We recommend future research focused on strengthening the relationship between farmers and social referents.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".