Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it