Structural-functionalism redux: adaptation to climate change and the challenge of a science-driven policy agenda
Bibliographic record
Abstract
Most efforts to develop a comprehensive, science-based approach to climate change adaptation have been written by natural scientists and resource managers and have adopted an underlying conception of policy-making as a functional process of mutual adjustment between elements of tightly linked natural and social systems. The influence of this framing is especially clear in the popularity of key metaphors such as ‘stress,’ ‘barriers,’ ‘vulnerability,’ and ‘resilience.’ There are obvious advantages to this way of proceeding, not least the possibility of using the systems concept as an overarching framework to integrate the multidisciplinary teams of researchers commonly employed in large-scale assessments of climate change impacts. Nonetheless, this underlying conception of linked natural and social systems presents significant challenges when it comes to moving the ideas found in these strategic documents forward into the world of policy and practice. As the case studies of North American, Australian, and European studies presented here show, the strategic documents themselves are very short on policy analysis, fail to incorporate the impact of institutions and policy legacies into their analyses, and, as a result, favor unfounded or infeasible management prescriptions. As a consequence, adaptation policy itself remains poorly developed in most jurisdictions.
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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 | 0.031 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.084 |
| Scholarly communication | 0.011 | 0.018 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.008 | 0.014 |
| Insufficient payload (model declined to judge) | 0.004 | 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".