The roles of governments and other actors in adaptation to climate change and variability: The examples of agriculture and coastal communities
Bibliographic record
Abstract
There is little question now about the reality of climate change and the importance of adaptation of human activities in reducing the negative impacts of climate change and variability (CCV) as well as the reduction of Greenhouse Gas Emissions in mitigating this unprecedented phenomenon. This article focuses on adaptation and the adaptive capacity of actors (decision-takers) of all sorts to adopt appropriate strategies and increase their adaptive capacity to cope with CCV by focusing on two types of human activity—agriculture and agricultural territories and coastal communities, both of which have very important roles to play in human society. Given the recent high profile given to the outcomes of COP21 and particularly the potential transfer of significant funding from developed to developing countries to support their battle against CCV, the emphasis has shifted again to the role of governments in this battle. We argue that governments have important roles to play both in developed and developing countries, but supporting funding of initiatives and for developing pertinent action plans is probably the least of our worries! Funding can be important but alone does not solve the challenges, it is what is accomplished with funding that is all important, and this requires the development of effective and pertinent adaptive capacities on the part of the different actors involved in what becomes a co-construction process. We argue that the roles of governments and other actors (collective as well as individual citizens and the activities that they are involved in) need to be better understood in order for this to happen. This is illustrated by research of different types on agriculture and coastal communities.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.014 | 0.027 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.005 | 0.004 |
| 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".