Bibliographic record
Abstract
Similar to other scientific areas, such as genetics and stem cell research, nanotechnology has sparked both public enthusiasm and social concern. Though hardly a new field of research, it is only recently that nanotechnology has caught attention of popular media and major public funding agencies. As a result of these developments, nanotechnology is now focus of policy discussions and ethical debates. In this special edition of Health Law Review we explore both scientific and social issues associated with nanotechnology. We have brought together a remarkable, interdisciplinary, collection of authors, including lawyers, philosophers, sociologists, communications experts, and nanotechnology scientists; perfect team to provide information about reality of current science and to analyse complex and still forming social concerns associated with nanotechnology. A theme that runs through many of papers in this collection relates to that surrounds nanotechnology. There are already a number of outspoken stakeholders that are both actively promoting as well as crusading against nanotechnology--a reality explored in paper by Einsiedel and McMullen. Groups such as Canada's ETC Group, for example, have gone so far as to call for a complete moratorium on nanotechnology research, portraying severe environmental and social concerns. Advocates of research, such as those within government who view nanotechnology as an important plank of emerging knowledge-based economy, emphasize theoretical benefits and commercial potential. Though these stakeholder groups often serve to facilitate public dialogue on important issues, they also tend to push debate to extremes and cloud public discussions with highly speculative risks and benefits. Regardless of source of hype, our experience with biotechnology shows that too much hype can be detrimental. It can adversely affect public trust, private investment and policy debate. As noted by Williams-Jones, if governments, academic scientists, and industry wish to effectively develop potential of nanoscience and nanotechnologies, they must be cognisant of dangers of over-hyping research and losing public trust. Countering hype must start with an appreciation of science and its likely applications. To this end, Tyshenko's piece covers controversial area of molecular nanotechnology. And by separating the reality of nanoscience and nanotechnology from fantasy, Wolkow's paper seeks to provide a more moderate vision of this field of study than is often found in popular press. …
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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 | 0.004 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.005 | 0.014 |
| Scholarly communication | 0.008 | 0.014 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".