Human rights in climate change adaptation policies: a systematic assessment
Bibliographic record
Abstract
Human rights have potential to enhance adaptation because they reflect internationally agreed upon standards of human dignity, aim to advance formal and substantive forms of equality, and can be used to hold public and private actors accountable for rights violations. We assess whether, how, and under what conditions national adaptation policies recognize human rights principles and standards. We analyze 217 adaptation policies from 147 countries to examine whether there is substantive recognition of the vulnerability and needs of equity-deserving groups that experience systemic marginalization and exclusion, and procedural inclusion of these groups in adaptation planning and decision-making. Results indicate that while under the Paris Agreement governments commit to respect human rights in their adaptation policies and actions, few countries are abiding by this commitment. Only one-third of countries refer to respect, promotion, or consideration of human rights within their adaptation policies. While most countries included here recognize specific conditions of different vulnerable groups in their policies, there is minimal evidence of their inclusion in the adaptation planning and decision-making process, and half of countries fail to identify specific measures that will be developed to reduce their vulnerability. None of the strategies that we reviewed pointed to the creation of accountability mechanisms for redressing harms that may arise due to adaptation actions. We also develop a series of regression models to examine whether hypothesized national predictors of adaptation action are associated with attention to human rights principles and standards. The models indicate that countries with greater wealth and equality are more likely to include attention to human rights norms in their adaptation strategies, but countries with less wealth, more inequality, and less political freedom appear to achieve a more substantive level of engagement with these norms in their strategies.
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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 | 0.115 | 0.211 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.019 | 0.019 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".