The trends and geography of nanotechnological research
Bibliographic record
Abstract
This paper presents a study of trends in nanotechnology, indicating regional development efforts, based on analyses of scientific publications from 17 countries, divided in two sets: seven key countries (USA, France, Germany, Japan, United Kingdom, Canada and Spain) and ten competitor-countries (Brazil, India, China, Australia, South Africa, Korea, Singapore, Malaysia, Israel and Mexico), from 1994 to 2004. A search in the Web of Science database was undertaken, utilizing 51 terms selected by experts in nanotechnology. A master dataset with almost 140,000 registers was created and scientific indicators were produced through data and text mining tools and a competitive intelligence approach. In the key countries, it was possible to discern the quantity of publications from the USA (21,769), followed by Japan (10,883). Within the per-country analysis, in the case of the USA, for example, the most frequently used terms are “nanoparticulates”, “nanotube”, “quantum dot”, “nanocrystal” and/or “nanostructure”. China has the best position in the competitor countries. Brazil is the best in the Latin America, and represents 5.7% of the competitor-country publications, with 1066 papers, and “quantum dot” is the most frequently term used for the representative Brazilian universities.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.016 | 0.035 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".