Documenting the state of adaptation for the global stocktake of the Paris Agreement
Bibliographic record
Abstract
Article 7, paragraph 14 of the Paris Agreement to the United Nations Framework Convention on Climate Change commits Parties to create a five yearly assessment of observed adaptation to track progress and enable appropriate future commitments through the Nationally Determined Contributions and National Adaptation Plans. No large‐scale study exists that shows the types of adaptation, the spatial distribution of types of adaptation, and the numbers of people engaging in that adaptation. To address this gap, and to feed into debates about the modalities for the global stocktake, in this paper we propose a new “stocktaking” approach to document the spectrum and prevalence of observed adaptation over large scales. The four‐step stocktaking approach focuses on: (a) obtaining consensus on the objectives of adaptation; (b) agreeing the sources of evidence; (c) agreeing the search method; and (d) categorizing the adaptations. By focusing on documenting rather than evaluating adaptation, the simple approach avoids some of the adaptation heuristic traps. With guidance to countries on how to operationalize, this approach could improve the transparency of adaptation data collection and analysis, ensure comparability of findings across space and time, and inform the Adaptation Communications (Article 7.10)—a prerequisite to strengthening future ambition commitments within the Paris Agreement. This article is categorized under: Policy and Governance > International Policy Framework Vulnerability and Adaptation to Climate Change > Institutions for Adaptation
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.
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 | 0.118 | 0.147 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.006 | 0.016 |
| Scholarly communication | 0.022 | 0.019 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.006 | 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".