Closing the Adaptation Gap in Mountains
Bibliographic record
Abstract
Over 1 billion people are living at the frontlines of climate change in mountain areas, where warming rates outpace the global average and are driving significant changes in environments and ecosystem services. These changes are exacerbating socioeconomic difficulties faced by many mountain communities, and are already intensifying vulnerabilities across mountain areas globally. The situation is indicative of pervasive and consequential deficits in adaptation, and calls attention to the need for a better understanding of existing adaptation efforts, as well as the prospects for increasing the quantity and quality of adaptation action in mountain regions. In response, this MountainAgenda article introduces a conceptual framework for adaptation gaps. It then uses data from 2 major global-scale adaptation reviews to shed light on the nature and true magnitude of the adaptation gap in mountains. It reveals shortcomings in available adaptation options, deficits in the uptake of existing adaptation support, and a general lack of coherence between existing adaptations and keystone global agreements relevant to climate change adaptation. These shortcomings are largely related to soft limits to adaptation that constrain responses across mountain areas. In this article, we provide recommendations for closing the adaptation gap in mountains and suggest that this will require deeply collaborative efforts that are rooted in local needs, aspirations, and ways of knowing, but that are also supported by external capacity building and implementation resources. In many instances, this will resemble a transformative approach to adaptation. The conceptual framework presented here is broadly applicable and can also be utilized to identify and close adaptation gaps in social-ecological contexts beyond mountains.
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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 | 0.009 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.001 | 0.006 |
| 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".