Innovation Trends in NAFTA Countries: an Econometric Analysis of Patent Applications
Bibliographic record
Abstract
This paper analyzes innovation trends in North America Free Trade Agreement (NAFTA) countries by means of the number of patent applications during the period 1965 to 2008.Making use of patent data released by the World Intellectual Property Organization (WIPO) and the Network for Science and Technology Indicators (Red Iberoamericana de Ciencia y Tecnología, RICYT), we search for presence of multiple structural changes in the patent applications series in Canada, Mexico, and the United States.Such changes may suggest that firms' innovative activity has been modified in these countries (Mansfield, 1986).Accordingly, it would be expected that the new regulations implemented in these countries in the 1980s and 1990s have influenced their intellectual property regimes through the NAFTA and the Trade-Related Aspects of Intellectual Property Rights (TRIPS) agreement.Consequently, the question conducting this research is how the new dispositions affecting intellectual regimes in NAFTA countries have affected innovation activities in these countries.The results achieved in this research confirm the existence of multiple structural changes in the series of patent applications resulting from the new legislation implemented in these countries.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 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".