The Ne<sup>3</sup>LS Network, Québec's initiative to evaluate the impact and promote a responsible and sustainable development of nanotechnology
Bibliographic record
Abstract
The spectacular progress made by nanosciences and nanotechnologies elicits as much hope and fear. Consequently, a great number of research and training initiatives on the e thical, e nvironmental, e conomic, l egal and s ocial issues regarding n anotechnology development (Ne 3 LS) are emerging worldwide. In Québec, Canada, a Task Force was mandated by NanoQuébec to conceive a Ne 3 LS research and training strategy to assess those issues. This Task Force brought together experts from universities, governments or industry working in nanosciences and nanotechnologies or in Ne 3 LS. Their resulting action plan, made public in November 2006, contained several recommendations, including the creation of a knowledge network (Ne 3 LS Network). In the following years, after consulting with numerous key players concerned with the possible impacts of nanosciences and nanotechnologies in Québec, the Ne 3 LS Network was launched in January 2010 in partnership with the Fonds québécois de la recherche sur la nature et les technologies, the Fonds québécois de la recherche sur la société et la culture and the Fonds de la recherche en santé du Québec, NanoQuébec, the Institut de recherche Robert-Sauvé en santé et en sécurité du travail as well as the University of Montreal. Its objectives are to 1) Foster the development of Ne 3 LS research activities (grants and fellowships); 2) Spearhead the Canadian and international Ne 3 LS network; 3) Take part in the training of researchers and experts; 4) Encourage the creation of interactive tools for the general public; 5) Facilitate collaboration between decision-makers and experts; 6) Involve the scientific community through a host of activities (symposium, conferences, thematic events); 7) Build multidisciplinary research teams to evaluate the impact of nanotechnology.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.115 | 0.010 |
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".