The competencies demonstrated by farmers while adapting to climate change
Bibliographic record
Abstract
World population growth, overconsumption of resources, competition among countries and climate change are putting significant pressure on agriculture. In Canada, changes in precipitation, the appearance of new pests and poor soil quality are threatening the prosperity of small farmers. What human competencies could facilitate citizens’ adaptation to climate change? The competencies displayed by six Canadian farmers were observed as they tried to improve the quality of their soil in order to increase its climate resilience. The farmers in the case study demonstrated a wide array of skills while adapting to climate change. Used to adjusting their farming practices to bad weather, the participants predicted that their already declining soil was very vulnerable to extreme events. They implemented some adaptations: planting forage radish and practicing more crop rotations. During the adaptation process, the farmers showed in-depth local and agricultural knowledge, critical thinking (which they used to assess the solutions), futures thinking and hindsight, identification and control of the variables affecting the crops, openness to novelty, collaboration, optimism and self-efficacy. The research, which results in the identification of competencies conducive to adaptation, leads to the recommendation of a few educational strategies to strengthen adaptive competencies when supporting citizens in a climate change adaptation process.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".