Rancher adoption and perception of greenhouse gas reducing practices in Canada
Bibliographic record
Abstract
Canada is home to over 39,000 beef farms and feedlots that are home to over 3.77 million beef cows. Beef cattle have a much larger carbon footprint compared to many other animal protein products. Given this context, it is important to understand producer mindsets regarding ranching practices and how they may help mitigate GHG emissions. Data for this paper was collected from two separate producer surveys that asked ranchers across Canada questions about their specific production practices and attitudes toward greenhouse gas emissions. Using this data, I apply cluster analysis techniques to identify four distinct groups of beef producers who vary on their willingness and ability to invest resources into changing practices to limit the production of greenhouse gasses (Willing and able, generally neutral, willing but unsure, and High Complexity Low Ability). In general, I find that there is a great deal of current adoption of management practices that have been shown to reduce greenhouse gas emissions. However, adoption is predicated on the impact of adoption on firm profitability as many respondents indicated that they would not be willing to change practices if these practices did not improve profitability. Greater adoption could be achieved through increased awareness relating to practices that have positive environmental impacts, particularly as they relate to greenhouse gas reduction and mitigation.
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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".