Trust, Risk, and Dissonance: Prairie Agriculture and Canada’s Environmental Farm Plan
Bibliographic record
Abstract
In the wake of combined economic and ecological pressure, Prairie farmers and the Canadian ministries responsible for agriculture are pressed to instigate sustainable agricultural development. However, Canada’s central agri-environmental program, the Environmental Farm Planning program (EFP), faces low uptake in the Prairie region. In this thesis, I explore the nature of the dissonance between the EFP and Prairie farmers to understand why participation is so low, the issues embedded in the EFP, and how to develop better agri-environmental policy for the Prairie region. I employ multiple methods, including survey, discourse, and institutional analyses, to make sense of the dissonance. Survey analysis is used to explore the social psychology of risk and characterize participant’s knowledge, risk perceptions, and trust regarding environmental action. Next, I employ discourse analysis to examine taken-for-granted notions embedded in how interviewees articulate their relationship to themselves, society, the environment, and the state. Finally, I utilize an institutional analysis to look at the mechanisms and values built into the EFP and the Prairie context and theorize how these institutional factors affect the EFP dissonance. Using the process of triangulation, I mix my methods to conclude that risk perceptions, economic constraints, and governmental trust are at the core of the EFP dissonance.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.030 | 0.024 |
| Scholarly communication | 0.011 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".