Human competences that facilitate adaptation to climate change: a research in progress
Bibliographic record
Abstract
Purpose For communities threatened by current or impending climate change, adaptation is becoming a necessity. Although little research has been done on human competences so far, this research shows that some appear to facilitate the adaptation process. The purpose of this multiple‐case study is to identify adaptive competences demonstrated by two groups of Canadian citizens: municipal employees in a coastal community and farmers. Design/methodology/approach As part of workshops based on a problem solving process, the two groups analyzed the impacts of climate change in their field of work and geographical area, chose a problem related to these impacts, suggested and then implemented adaptation measures. The municipal employees worked on sea level rise, whereas the farmers focused on poor soil quality, which makes it vulnerable to bad weather. Findings By thematically analyzing the verbatim transcripts of the workshops and by building narratives, the authors were able to identify similar adaptive competences in both groups: local knowledge, futures thinking, hindsight, risk prediction, critical thinking, decision‐making, and problem solving (highlighting key problem components, suggesting solutions, and identifying constraints). However, two competences were chiefly found in the group composed of farmers: optimism and openness to novelty. Originality/value This study is one of the first to lead to recommendations regarding the pedagogical support of citizens during an adaptation process to climate change. These recommendations might be helpful in many communities where adaptation to climate change is a pressing issue.
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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 | 0.023 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.006 | 0.011 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".