Avoiding Environmental Catastrophes: Varieties of Principled Precaution
Bibliographic record
Abstract
The precautionary principle is often proposed as a guide to action in environmental management or risk assessment, and has been incorporated in various legal and regulatory contexts.For many, it reflects the common sense notion of being safe rather than sorry, but it has attracted numerous critics.At times, proponents and critics talk at cross purposes, due to the multiplicity of ways the precautionary principle has been formulated.The approach taken here is to examine four general varieties of precaution, relating each to arguments made in various contexts by others.First, I examine the parallel between the precautionary principle and an argument referred to as Pascal's wager.Critics are right to dismiss versions of the precautionary principle that follow the logic of Pascal's wager, because that argument requires assumption of an infinite catastrophe, which is seldom the case in environmental decisions.Second, I explore precaution viewed as an instance of the phenomenon of ambiguity aversion as described by Daniel Ellsberg.Third, I evaluate precautionary perspectives on our duties to future generations, drawing inspiration from the views of Gifford Pinchot.Fourth, I consider the precautionary principle as an instance of Aldo Leopold's notion of intelligent tinkering.Although controversy persists, I find that a legitimate theoretical foundation exists to implement Ellsbergian, Pinchotian and Leopoldean varieties of precaution in environmental decision making.Additionally, I remark on the role of adaptive management and maintaining resilience in ecological and social systems as an approach to implementing the precautionary principle.
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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 | 0.012 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.062 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.008 | 0.009 |
| Insufficient payload (model declined to judge) | 0.003 | 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".