Towards a Practical Climate Ethics: Combining Two Approaches to Guide Ethical Decision-Making in Concrete Climate Governance Contexts
Bibliographic record
Abstract
This paper discusses two approaches to climate ethics for practical reflection and decision-making in concrete local climate change governance. After a brief review of the main conceptual frameworks in climate ethics research, we show that none of these leading approaches is sufficiently context specific and pluralistic to provide guidance appropriate for concrete local climate governance. As alternatives, we present principlism as a methodology of mid-level principles and environmental pragmatism as an ethical approach. We argue that the two methodologies of principlism and pragmatism offer a new pluralistic framework that allows real-world conditions and contexts to be properly integrated into ethical analysis and decision-making in climate governance.
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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 | 0.072 | 0.048 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.006 | 0.074 |
| Scholarly communication | 0.022 | 0.020 |
| Open science | 0.004 | 0.018 |
| Research integrity | 0.009 | 0.013 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".