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Innovation Trends in NAFTA Countries: an Econometric Analysis of Patent Applications

2011· article· en· W2056811888 on OpenAlexaboutno aff
José Carlos Rodríguez, Mario Gómez

Bibliographic record

VenueJournal of technology management & innovation · 2011
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicFirm Innovation and Growth
Canadian institutionsnot available
Fundersnot available
KeywordsIntellectual propertyTRIPS architectureInternational tradeTRIPS AgreementEconometric analysisLegislationDeveloping countryBusinessWorld tradeFree trade agreementEconomicsInternational economicsPolitical scienceFree tradeEconomic growthLawEngineeringEconometrics

Abstract

fetched live from OpenAlex

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 Tecnologa, 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 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.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesBibliometrics
Consensus categoriesBibliometrics
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.664
Threshold uncertainty score0.973

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0440.047
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.077
GPT teacher head0.258
Teacher spread0.182 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations9
Published2011
Admission routes1
Has abstractyes

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