Factors that Influence the Number of Patent Deposited in some Countries of the American Continent
Bibliographic record
Abstract
The information technology available in the world is disclosed only in the form of patent documents deposited, which also reflect the scientific and technological level of a country. This paper has the objective of identifying what are the main factors that influence the generation of these patents in some American countries. A modeling study via multiple linear regression to analyze the effects of the number of published articles, gross domestic product (GDP), population and variations in number of patents over the last year and the last two years, about number of patents in those countries. The patent data were obtained from the World Intellectual Property Organization, data on GDP and population come from the World Bank and the amount of scientific articles from the SCImago Journal & Country Rank. Based on the selection criteria, the countries chosen for modeling were United States, Canada, Brazil, Mexico, Colombia, Chile and Argentina. The results indicated that in the United States the number of patents increases as GDP. In all other countries, the variation in the number of patents in relation to two recent years contributes to an increase in the number of patents. In Brazil, Argentina and Chile, in addition to this variation, an increase of the population favors the patent number. In Canada and Colombia, the number of patents also increases according to the number of articles published. In Mexico, the variation in two years and the GDP contributed to the increase in the number of patent.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".