Evaluating global technology transfer research performance
Bibliographic record
Abstract
Technology transfer is one of the most important fields in research and development of new products and new technology knowledge services, technology transfer also one of key issues in knowledge economics era. This study evaluates the global technology transfer development trend of research for the past sixteen years and provides insights into the characteristics of technology transfer research activities to identify development map, tendencies, or regularities that may exist in papers. Data are based on the online version of SCI, Web of Science from 1992 to 2008. Articles referring to technology transfer were assessed according to many aspects including logarithmic model fitting publication outputs during 1992-2007. The result displays that the USA is number one in technology transfer research totaling 447 papers, followed by UK totaling 150 papers. Other leading countries in technology transfer research include Germany, Switzerland, Italy, Canada, Australia and France. This new bibliometric method can help researchers realize the panorama of global technology transfer research, and establish further research direction.
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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 | 0.035 | 0.102 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.053 | 0.070 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".