Mapping the ethical landscape of wildland fire management: setting an agendum for research and deliberation on the applied ethics of wildland fire
Bibliographic record
Abstract
Background Virtually every decision within wildland fire management includes substantial ethical dimensions. As pressures increase with ever-growing fires, it is becoming increasingly important to develop tools for assessing and acting on the values intrinsic to wildfire management. Aims This paper aims to foster an applied ethics of wildland fire by bringing values to the forefront of wildland fire management debates, highlighting areas where ethical issues have been previously discussed, and providing a framework to assist in future discussion. Methods Through a literature review and collaborative thematic coding of a large set of ethical dilemmas, a list of ethical lenses was developed. Key results Five ethical lenses were generated from the thematic coding process: Epistemologies and Representation, Values and Priorities, Risk and Uncertainty, Power, and Metaethics. Conclusion The five lenses provide a framework to identify prospective ethical tensions in wildland fire decision-making, both within and cutting across categories. This framework provides a way of structuring future investigations into wildfire ethics, as well as a starting point for developing techniques to integrate community and stakeholder values. Implications Developing a field of applied ethics for wildland fire will help support decision-making, create space to more inclusively reflect and deliberate on values, and ensure that fire management best serves the public interest.
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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 | 0.178 | 0.111 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.010 | 0.006 |
| Science and technology studies | 0.027 | 0.056 |
| Scholarly communication | 0.032 | 0.035 |
| Open science | 0.005 | 0.030 |
| Research integrity | 0.011 | 0.013 |
| 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".