Why is grazing management being overlooked in climate adaptation policy?
Bibliographic record
Abstract
In 2018, two studies were conducted by Canada’s Parliament on the connections between climate change and agriculture. Links between grazing management and climate change adaptation and mitigation are included in the testimonies gathered during these studies but the resulting final reports are silent on the topic. Analysis of 112 parliamentary files revealed insights on (1) the knowledge about grazing management that was omitted from the two final reports, (2) the social contexts that informed the processes of hearing testimonies and developing the reports, and (3) the underlying ideologies and normative assumptions reflected in these studies. Overall, the current state of policy regarding climate change and agriculture emphasizes technical, scientific, and expensive solutions, and as a result, the benefits of grazing management are overlooked. We argue transformations toward sustainable, climate adaptive agriculture require an ongoing examination of how political structures, knowledge hierarchies, and underlying ideologies inform and narrow policy outcomes.
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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".