Enhancing national innovative capacity: The impact of international trade and foreign investment
Bibliographic record
Abstract
Innovation productivity differs across economies and latecomer countries are working hard to close their gap with developed countries. This study is to explore what affects national innovative capacity by incorporating international trade and inward foreign investment as two key determinants of country-level production of international patents. An investigation of 80 countries during the years 1981-2010 shows that four major variables account for the variation in international patenting activities across countries: patent stocks, levels of R&D manpower, industrial specialization, and quality of linkage. We also find that both high-tech related international trade and inward foreign direct investment significantly contributes to emerging countries’ ability to produce cutting-edge technologies, although this effect does not exist for leading innovator countries. Moreover, although strong intellectual property rights (IPRs) protection is highly correlated with international patenting activities in leading innovator countries, it is found to have a negative impact on emerging innovator countries’ national innovative capacity. This study thus helps better understand the role of international economic activities and IPRs in enhancing national innovative capacity, especially for emerging countries to catch up with leading innovator countries.
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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".