Climate Change‐Conscious Methodologies: Ethical Research in a Changing World
Bibliographic record
Abstract
ABSTRACT Changes in the frequency and intensity of climate‐related disasters are changing the social landscape for environmental research. Even in the most optimistic scenarios, the proportion of researchers forced to deal with the effects of climate change will continue to grow. Methodologies across disciplines need to be adaptable to meaningfully address the ethical and practical challenges of conducting research in an increasingly disaster‐prone world. In this article, we draw on insights from fields including disaster and emergency literatures and our personal experiences as researchers directly impacted by climate disasters to put forward a framework for climate change‐conscious research methodologies. This review offers considerations for ethical research in climate change‐affected communities and outlines critical areas for future research.
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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 | 0.273 | 0.215 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.010 | 0.094 |
| Scholarly communication | 0.020 | 0.017 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.009 | 0.013 |
| Insufficient payload (model declined to judge) | 0.003 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".