The trends and geography of nanotechnological research
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 it