Adaptation pathways to inform policy and practice in the context of development
Bibliographic record
Abstract
Adaptation pathways are a decision-focused approach to account for future uncertainties and complexities in planning and implementation of adaptation actions. The pathways approach incorporates flexibility into decision making to accommodate for changing conditions over time, and to reduce undesirable path dependencies and maladaptive consequences. While the pathways approach for adaptation planning has received great interest from both climate scientists and practitioners, there has been little specific guidance on how to implement them and how to sustain the resulting outcomes. Accordingly, pathways approaches include diversified methodologies, with scope for reorienting and adjusting methods for different decision contexts. This special issue explores both theoretical and empirical cases of adaptation pathways in different contexts. A learning framework on adaptation pathways has been developed from a systematic review of adaptation literature. In this editorial, the framework is used to characterize the twelve case studies presented in the special issue, followed by a synthesis of lessons which point to some critical research gaps in adaptation pathways.
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 | 0.037 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.006 | 0.028 |
| Scholarly communication | 0.020 | 0.036 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.011 | 0.013 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".