Analysis on the Factors Influencing the International Technology Transfer Based on ISM Model
Bibliographic record
Abstract
The technology is not only an indispensable element involved in international trade, but also an important factor affecting the comparative advantage and trade patterns in international trade. Based on predecessors’ research and practice experience, this dissertation selects 20 factors to study the influencing factors, such as international technology transfer, the construction of infrastructure, the complexity of technological progress, economic development level and so on. By using the ISM model, the paper studies the correlation and gradation of influencing factors of international technology transfer. The analysis indicates there are 4 direct factors on surface and. 6 factors on path: the applicability and negotiability of the technology, international technology transfer intermediary. 3 direct factors: the construction of infrastructure. 5 indirect factors: environment changes of international economy, the complexity of technological progress. 2 factors in deep roots: economic development level and changes of industrial structure. Based on this, the paper puts forward corresponding countermeasures and suggestions from five aspects. Meanwhile, it provides certain references to improve the international technology transfer level, promote using international technology transfer to improve technology level, and upgrade the industrial structure.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".