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Record W2545406675 · doi:10.1080/11926422.2016.1190771

Regulatory barriers to international scientific innovation: approving new biotechnology in North America

2016· article· en· W2545406675 on OpenAlexaffabout
Stuart J. Smyth, William A. Kerr, Richard Gray

Bibliographic record

VenueCanadian Foreign Policy Journal · 2016
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicIntellectual Property and Patents
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsMultinational corporationBusinessInternational tradeTechnology transferIndustrial organizationInvestment (military)Foreign direct investmentInternational economicsPolitical scienceEconomicsFinance

Abstract

fetched live from OpenAlex

The regulation of science often differs among countries and leads to a divergence that can create barriers to research, knowledge transfer and product trade. Many types of scientific research are mobile activities that be done in a number of countries for a comparable cost. With technology development firms facing comparable development costs regardless of location, it is well understood that location decisions are driven by the time and monetary costs of compliance with regulatory systems. In this article we show the compatibility of a country's regulatory system with foreign regulatory systems can also impact the viability of research investment. We argue there may be considerable economic gain from bilateral, and eventually multinational, agreements to adopt a harmonized or shared regulatory process. This is particularly germane for the United States and Canada which are already in a free trade agreement.

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 imitation

Not 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.

metaresearch head score (Codex)0.011
metaresearch head score (Gemma)0.025
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.721
Threshold uncertainty score0.554

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.025
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0070.006
Scholarly communication0.0100.004
Open science0.0010.004
Research integrity0.0040.004
Insufficient payload (model declined to judge)0.0050.000

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 head0.052
GPT teacher head0.219
Teacher spread0.167 · 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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations5
Published2016
Admission routes2
Has abstractyes

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