Ethics, climate change and health – a landscape review
Bibliographic record
Abstract
Anthropogenic climate change is unequivocal, and many of its physical health impacts have been identified, although further research is required into the mental health and wellbeing effects of climate change. There is a lack of understanding of the importance of ethics in policy-responses to health and climate change which is also linked to the lack of specific action-guiding ethical resources for researchers and practitioners. There is a marked paucity of ethically-informed health input into economic policy-responses to climate change-an area of important future work. The interaction between health, climate change and ethics is technically and theoretically complex and work in this area is fragmentary, unfocussed, and underdeveloped. Research and reflection on climate and health is fragmented and plagued by disciplinary silos and exponentially increasing literature means that the field cannot be synthesised using conventional methods. Reviewing the literature in these fields is therefore methodologically challenging. Although many of the normative challenges in responding to climate change have been identified, available theoretical approaches are insufficiently robust, and this may be linked to the lack of action-guiding support for practitioners. There is a lack of ethical reflection on research into climate change responses. Low-HDI (Human Development Index) countries are under-represented in research and publication both in the health-impacts of climate change, and normative reflection on health and climate change policy. There is a noticeable lack of ethical commentary on a range of key topics in the environmental health literature including population, pollution, transport, energy, food, and water use. Serious work is required to synthesise the principles governing policy responses to health and climate change, particularly in relation to value conflicts between the human and non-human world and the challenges presented by questions of intergenerational justice.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".