Exploring Prince Edward Island Beef Farmer Perceptions of Participation in Climate Change Mitigation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Beef farmers play a critical role in addressing climate change through the adoption of management practices that reduce or sequester greenhouse gas emissions. However, farmers’ perceptions of their role in mitigation strongly shape their willingness to participate in such practices. This study draws on identity theory to examine how beef farmers on Prince Edward Island (PEI), Canada, frame their participation in climate change mitigation. Framing analysis was used to explore the identities farmers express in relation to their environmental actions. Using a qualitative case study approach, the research employed purposive sampling to conduct in-depth interviews with nine PEI beef farmers actively engaged in mitigation-related farm practices. Data were analyzed through framing analysis within an identity theory framework to identify recurring themes and identity-based patterns. Four primary identity frames emerged: productivist, conservationist, hero, and scientist. Most farmers drew on multiple identities when discussing their environmental actions, with the productivist frame being the most prevalent. These identity frames shaped how farmers understood and articulated their role in climate change mitigation, with participation often grounded in practical, production-oriented motivations rather than environmental ideology. The study highlights the need for climate communication strategies that reflect the multiple identities farmers hold. Messages that align with the dominant productivist identity, while still integrating conservationist and scientific values, may be particularly effective in encouraging mitigative practices. Future research should investigate the perceptions of farmers not currently engaged in mitigation to further broaden engagement strategies.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it