Implications of the Precautionary Principle in research and policy‐making
Bibliographic record
Abstract
The Precautionary Principle (PP) has recently been formally introduced into national and international law. The key element is the justification for acting in the face of uncertainty. The PP is thereby a tool for avoiding possible future harm associated with suspected, but not conclusive, environmental risks. Under the PP, the burden of proof is shifted from demonstrating the presence of risk to demonstrating the absence of risk and it is the responsibility of the producer of a technology to demonstrate its safety rather than the responsibility of public authorities to show harm. Past experiences show the costly consequences of disregarding early warnings about environmental hazards. Today, the need for applying the PP is even greater. New research is needed to expand current insight into disease causation, to elucidate the full scope of potential adverse implications resulting from environmental pollutants, and to identify opportunities for prevention. Research approaches should be developed and strengthened to counteract innate ideological biases and to support our confidence in applying the PP for decision-making in the public policy arena.
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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 | 0.186 | 0.207 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.010 | 0.104 |
| Scholarly communication | 0.020 | 0.032 |
| Open science | 0.006 | 0.015 |
| Research integrity | 0.029 | 0.029 |
| Insufficient payload (model declined to judge) | 0.009 | 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".