Redefining climate change maladaptation using a values‐based approach in forests
Bibliographic record
Abstract
Abstract Climate change adaptation can have unexpected and detrimental effects, typically conceptualized as maladaptation and narrowly defined in relation to climatic hazards and climate vulnerability. We revisit this narrow framing of maladaptation using a deliberative risk analysis method in 16 focus groups across British Columbia, Canada, where forests are crucial to social, economic and environmental well‐being. By analysing emergent logics of support and opposition around genomics‐based assisted migration as an adaptation strategy in forests, we identify four sources of potential maladaptation in this context: technical failure, opportunity cost, path dependence and the too‐narrow framing of adaptation. Combined, these suggest that maladaptation is also too narrowly conceptualized, reflecting an obsolete definition of adaptation as rational adjustment to climatic hazards. Rather than being a failure of adaptation, per se, we argue that maladaptation comprises climate‐adaptive policies or actions that, in a broader frame, threaten the very values that decision‐makers ostensibly seek to protect and enhance. A free Plain Language Summary can be found within the Supporting Information of this article.
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 | 0.012 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.010 | 0.022 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| 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".