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Record W2766035516

Investor-State Dispute Settlement: Human Rights and Regulatory Lessons from Lilly v Canada

2017· article· en· W2766035516 on OpenAlexaboutno aff
Daniel J. Gervais

Bibliographic record

VenueUC Irvine law review · 2017
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicInternational Arbitration and Investment Law
Canadian institutionsnot available
Fundersnot available
KeywordsInvestor-state dispute settlementContext (archaeology)Human rightsIntellectual propertyRevocationState (computer science)Law and economicsSettlement (finance)LawEnforcementComplaintPolitical scienceBusinessEconomicsEngineeringForeign direct investmentFinanceInternational investment
DOInot available

Abstract

fetched live from OpenAlex

The triangular interface between trade, IP and human rights has yet to be fully formed, both doctrinally and normatively. Adding investor-state dispute-settlement (ISDS) to the mix increases the complexity of the equations to solve. Two resultant issues are explored in this Article. First, the Article considers ways in which broader public interest considerations-in particular human rights -- can and should be factored into determinations of a state’s action compatibility with its trade obligations and commitments in a state-to-state dispute-settlement context. Second, the Article examines whether doctrinal tools used in state-to-state trade dispute-settlement to make room for public interest considerations port to the investment/ISDS context. The Article uses the recent Lilly v Canada case as backdrop to illustrate the points made. The Lilly case dealt with an ISDS complaint filed after the revocation of two Canadian patents on pharmaceutical products. The Article approaches the above-mentioned triangular interface from a policy perspective that factors in innovation and investment protection but also public health, a policy area supported by a human right (to health) and in which states need regulatory autonomy.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.893
Threshold uncertainty score0.772

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.025
GPT teacher head0.264
Teacher spread0.240 · 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

Citations4
Published2017
Admission routes1
Has abstractyes

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