Applying a precautionary risk management strategy for regulation of nanotechnology
Bibliographic record
Abstract
Nanotechnology promises a multiplicity of benefits to society. At the same time it has become a focus of debate regarding potential health and other associated risks. Rejection of new nanotechnology innovations could result in loss of trust in regulators, a phenomenon observed previously with nuclear and genetically altered food crop technologies. Due to this uncertainty a precautionary approach is warranted. The anticipated four stages of nanotechnology development, from passive to more active forms, are arrayed against existing risk management strategies of a precautionary nature. The overlay suggests that precaution is appropriate for all stages of nanotechnology development. Other effects from innovation, such as socio-economic inequity, disruptive impact on labour markets, alteration of global trade and unintended health and environmental impacts, can also be minimised by applying a precautionary approach. The use of a precautionary approach can provide protection to developers of nanotechnology, to individuals and to the environment.
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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 | 0.034 | 0.030 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.004 | 0.013 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.011 | 0.008 |
| Insufficient payload (model declined to judge) | 0.004 | 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".