A framework for explaining the links between capacity and action in response to global climate change
Bibliographic record
Abstract
Although great strides have been made towards a more nuanced understanding of the impacts and causes of global climate change, the ability to design and implement policy responses that engender effective action has remained insufficient. Recent framings of adaptive capacity and mitigative capacity are built upon in this article, and response capacity is introduced as a useful way to integrate adaptation and mitigation within the context of underlying development paths. In tracing the complex and non-linear relationships between response capacity—which represents a broad pool of development-related resources that can be mobilized in the face of any risk—and real policy and behaviour change in response to climate change, the strong influence of manifold socio-cultural factors is revealed. Only through an analysis of these deeper trajectories can the most important barriers to action begin to be addressed. Theories of risk perception are drawn upon to elucidate the complex nature of the relationship between capacity and action. A deeper understanding of these relationships will aid in the design and implementation of adaptation and mitigation policies that more effectively address the multitude of temporally and contextually specific intricacies of human behaviour in response to risks such as climate change. The literatures of institutional genesis and change, sociotechnical systems, social movements, and collective behaviour change theory (to name but a few) are argued to be crucial to an improved understanding of the underlying development paths which influence both capacity and action.
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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 | 0.006 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.025 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 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".