Climate Change Perceptions and Associated Characteristics in Canadian Prairie Agricultural Producers
Bibliographic record
Abstract
Climate change (CC) poses a threat to agricultural sustainability, which is important in the Canadian Prairies, as agriculture is a major occupation and driver of the economy. Agriculture involves both the creation and mitigation of emissions related to CC. To implement adaptation and mitigation practices, producers should accept CC as fact. This study is based in Saskatchewan, Canada, where CC denial is prevalent in public comments. To assess the validity of this anecdotal impression, this study provided a snapshot of Saskatchewan agricultural producers’ perceptions and observations of CC and assessed whether views on CC are associated with characteristics of political orientation and affiliation, mental flexibility, systems thinking, time orientation, climate knowledge, climate observations, and demographic variables. A survey was developed with the following four sections: (1) individual characteristics; (2) observed changes in climate-related variables; (3) knowledge and perceptions about CC; and (4) demographic variables. The survey included multiple-choice questions and items scored on a Likert scale. The survey was completed by 330 Saskatchewan agricultural producers (i.e., farmers and ranchers). The results indicated more CC denial in Saskatchewan producers than in other Canadian samples. Individual and socioeconomic characteristics of lower levels of formal education, identifying as male, conservative political affiliation and ideation, low trust in science, and low mental flexibility were associated with less acceptance and concern of CC. It is therefore necessary to consider socioeconomic and individual characteristics of producers in measures aiming to increase the acceptance of the reality of CC. Future intervention research should target male producers with lower levels of formal education, low trust in science, low mental flexibility, and right-leaning political ideation for the improvement of CC perceptions and examine different teaching methods (e.g., lectures, workshops, webinars) and dissemination methods (e.g., online versus in-person sessions) to see how various techniques may influence learning, as well as the way the information is used by particular groups.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".