Of Climate and Weather: Examining Canadian Farm and Livestock Organization Discourses from 2010 to 2015
Bibliographic record
Abstract
Abstract Producer organizations representing Canada’s farm and livestock sectors are powerful change agents and advocates for their industries, particularly during challenging times such as climate- or weather-related hardships. Such organizations have a complex role: engaging with policy-makers, as well as their memberships and the public, to pursue the interests of their specific communities. This paper includes an examination of how farm producer organizations communicate about climate and weather to these various audiences, and the specific needs and recommendations they advance. Of particular interest are commodities related to pasture-based grazing, which is underrepresented in the climate adaptation literature. A collection of 95 publicly available documents is analyzed, representing a snapshot of climate- and weather-related public and policy engagement of Canadian and Albertan farm and livestock producer organizations from 2010 to 2015. Qualitative coding by scale, commodity, and audience revealed three significant patterns within this exploratory study. First, while national “umbrella” organizations speak climate to government, Alberta-based livestock/forage organizations speak to their members with a focus on weather. Second, while the two national umbrella organizations examined are politically divergent, they appear to be united on the topic of climate change. Third, common ground was also found between climate and weather discourses around on-farm management, specifically rotational grazing. These three patterns reveal a disjointed dialogue within the Canadian farm and livestock sectors on topics of climate adaptation and mitigation, as well as opportunities for future cooperation, and the need for further research on farm organization beliefs and their capacity to create/manage climate knowledge.
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.010 |
| Science and technology studies | 0.020 | 0.007 |
| Scholarly communication | 0.009 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".