Evaluation innovation research performance and trend of the worldwide
Bibliographic record
Abstract
Innovation is one of the most important fields in research and development of new knowledge or service today, making research innovation trend is an important issue. This study evaluates the worldwide innovation development trend of research for the past sixteen years and provides insights into the characteristics of innovation research activities to identify an innovation development map, tendencies, or regularities that may exist in papers. Data are based on the online version of SSCI, Web of Science from 1993 to 2008. Articles referring to innovation were assessed according to many aspects including exponentially fitting publication outputs during 2002–2008, distribution of source title, author keywords, and keyword plus analysis. The exponential fitting of the yearly publications of the last decade can also calculate that, in 2014, the number of scientific papers on innovation will be twice the number of publications in 2008. Synthetically analyzing four kinds of keywords, this work analysis concludes that innovation application relates to issues based on knowledge, technology, R&D and entrepreneurship. The result displays that the USA is number one in innovation research totaling 6,317 papers, followed by UK totaling 2,354 papers. Other leading countries in innovation research include Canada, the Netherlands, Germany, France, Australia and Italy.
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.
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 | 0.019 | 0.068 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.027 | 0.037 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".