Past and future adaptation pathways
Bibliographic record
Abstract
Adaptation pathways are increasingly being used as a foresight tool to help guide the implementation of climate change adaptation and deliberate transformation. This paper applies a pathways lens as a hindsight tool to provide new understanding about past change and adaptation relevant for improving future adaptation pathways approaches. Four case studies of past adaptations to change are examined: Solomon Islands Communities, Canadian forest-dependent communities, a Transylvanian village, and responses to climate adaptation policies in Australia. The results highlight that responses to change in these diverse case studies involve complex transitions that gradually create new conditions and trajectories; manifest as multiple but inter-related pathways of change and response at different social or spatial scales (e.g. different paths for different households or communities); have legacies and continuities across time that affect future pathways of change; are affected by power in complex ways; and can create further change and need for adaptation. Analyses also highlight that when working with prospective adaptation approaches as a response to climate change there is a need to consider: (1) underlying assumptions, values and principles associated with the future; (2) the existence of inter-connected multiple pathways and their implications for reinforcing existing social inequalities; and (3) how understanding past change provides inspiration for new and transformative futures. Overall, the paper concludes that shifts towards analyses for change rather than simply about change, such as adaptation pathways, will require more careful consideration of underlying ontological assumptions about the relationships between past, present and future.
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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 | 0.011 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.007 | 0.018 |
| Scholarly communication | 0.012 | 0.023 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.017 | 0.001 |
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".