Tipping Toward Transformation: Progress, Patterns and Potential for Climate Change Adaptation in the Global South
Bibliographic record
Abstract
In response to observed and projected climate change impacts, major donors are funding an abundance of climate change research in the global South. The product of these funding schemes is often an abundance of cases with little attention paid to capturing the broader trends and patterns across cases. Furthermore, calls are increasingly being made for both adaptation and mitigation policies that are transformative: strategies that tackle the roots of vulnerability and high carbon development pathways to create a more fundamental shift towards sustainability. In this paper, we assess 54 cases of donor-funded adaptation research in the global South to paint a detailed picture of the types of adaptation options being proposed and implemented, their scope and the intended beneficiaries. We consider these data through the lens of transformation: to what extent do these cases illustrate adaptation actions that might push the social-ecological system over a tipping point towards a more desirable, sustainable state? Ultimately, we find that the adaptation options in these cases focus on educational or behavioral campaigns rather than deeper governance, legislative, or economic shifts. Similarly, the scale of action most often targets communities, rather than ecosystems, watershed, or regional/national scales. Even so, the emergence of resilience thinking in some projects, and the potential for a values shift triggered by these projects may sow the seeds of a longer-term transformation, if more attention is paid to synergies between development objectives and climate change actions.
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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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.001 | 0.002 |
| 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".