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Record W3115641004

International Trade, Intellectual Property, and Innovation Policy: Long-Term Lessons from the COVID-19 Crisis

2020· article· en· W3115641004 on OpenAlexaffabout
De Beer, Jeremy, E Richard

Bibliographic record

VenueSSRN Electronic Journal · 2020
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicIntellectual Property and Patents
Canadian institutionsMcGill UniversityUniversity of Ottawa
Fundersnot available
KeywordsIntellectual propertyPandemicPreparednessCoronavirus disease 2019 (COVID-19)International tradeLaw and economicsPolitical scienceInternational lawBusinessEconomicsLawInfectious disease (medical specialty)
DOInot available

Abstract

fetched live from OpenAlex

This chapter addresses intersections among international trade law, intellectual property rights, and domestic innovation policies to prevent, detect, and treat pandemics. Structural issues with Canada’s innovation system affected preparedness for this pandemic and, unless remedied, will impede responses to future crises. In this chapter, we suggest aligning domestic and international policy measures to nuance Canada’s approach to intellectual property and accelerate Canada’s global contributions through open science.

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)0.002
Version: codex-gemma-dda1882f352aValidation 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: none
Teacher disagreement score0.752
Threshold uncertainty score0.570

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.001
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 head0.130
GPT teacher head0.289
Teacher spread0.159 · 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 teacher head, 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

Citations0
Published2020
Admission routes2
Has abstractyes

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