Biodiversity in Canadian Dairy: Uncovering Opportunities for Meaningful Change in the Canadian Dairy Industry’s Environmental Stewardship
Bibliographic record
Abstract
This research seeks to understand Canadian dairy farmers’ motivations for incorporating or not biodiversity into their farms. It is widely understood that yield, and especially farm-level sustainability, can be improved by incorporating biodiversity at the farm level because it increases ecosystem health and resilience. But, biodiversity and ecosystem services are not clearly connected in the literature to larger environmental repairs. This project began with a national survey of Canadian dairy farmers, focusing on implementation of biodiversity, and barriers impeding implementation. We are currently pursuing case studies of selected farms to examine the reasons for and outcomes of incorporating biodiversity practices. We expect to find that not adopting biodiversity practices will be tied to implementation costs, combined with anticipated reduction in profit. However, Merchant (2003), suggests that we will also discover underlying cultural values and beliefs about control over land and the right to individual profit contributing to this resistance.
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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".