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Record W2951000532 · doi:10.1089/ind.2019.29170.lgb

Protection and Commercialization of Biotechnology Inventions in Canada and Québec

2019· article· en· W2951000532 on OpenAlexaffabout
Louise Bernier

Bibliographic record

VenueIndustrial Biotechnology · 2019
Typearticle
Languageen
FieldMedicine
TopicBiomedical Ethics and Regulation
Canadian institutionsUniversité du Québec
Fundersnot available
KeywordsCommercializationIntellectual propertyContext (archaeology)NegotiationGovernment (linguistics)BiopharmaceuticalBusinessIncentiveInternational tradeIndustrial organizationBiotechnologyPolitical scienceEconomicsMarketingLawMarket economy

Abstract

fetched live from OpenAlex

An extensive body of evidence demonstrates that patents and other intellectual property (IP) rights are critical to the future of innovation and the development of new treatments and cures. A strong legal regime is essential for a robust innovation-based biopharmaceutical industry, but other targeted incentives can provide further impetus to transform inventions into commercial innovations. For Canada, the legal context surrounding intellectual property rights protection and the national regulatory regime are influencing the biopharmaceutical industry. These dimensions also have consequences for Canadian patients, Canadian economy, and access to future medical innovations. In the course of trade negotiations, several aspects of the Canadian IP system have been changed and reinforced. This article summarizes the biotech industry context and describes existing IP policy and regulatory initiatives in Canada. We will also discuss briefly the most recent initiatives of the Québec government to support creation, protection and commercialization of innovations from local businesses.

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)0.004
metaresearch head score (Gemma)0.008
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: Other · Consensus signal: Other
Teacher disagreement score0.787
Threshold uncertainty score0.913

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0030.004
Science and technology studies0.0070.004
Scholarly communication0.0060.001
Open science0.0010.001
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0060.000

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 head0.035
GPT teacher head0.246
Teacher spread0.211 · 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
GenreOther

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

Citations1
Published2019
Admission routes2
Has abstractyes

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