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Record W3162391529

Patents and Plant Breeders' Rights: Approaches to Intellectual Property Overlaps

2018· article· en· W3162391529 on OpenAlexaffabout
Jeremy de Beer

Bibliographic record

VenueSSRN Electronic Journal · 2018
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicIntellectual Property and Patents
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsHumanitiesIntellectual propertyPolitical scienceDissenting opinionJurisprudenceNousEthnologyLawSociologyPhilosophy
DOInot available

Abstract

fetched live from OpenAlex

English Abstract: This article addresses overlaps between patents and plant breeders’ rights. To do so, it examines language that judges in the United States and Canada have used in deciding whether to allow cumulative protection for the same subject-matter by both kinds of intellectual property rights. Distilling the core arguments from a series of judgments during the last four decades, the article explains three themes underpinning the case law on overlaps among patents and plant breeders’ rights. Majority and dissenting opinions consider overlaps in terms of: the adequacy of incentives, the potential for inconsistency, and/or the historical logic of legislative drafting. These considerations may determine the outcome of future cases in which overlapping protection is at issue. French Abstract: Dans cet article, l’auteur traite des chevauchements entre les brevets et la protection des obtentions ve ge tales. Pour ce faire, il examine le langage utilise par les juges aux E tats-Unis et au Canada au moment de de cider s’il y a lieu d’autoriser que le meˆme objet rec oive une protection cumulative en vertu des deux types de droits de proprie te intellectuelle. Re sumant les principaux arguments d’une se rie d’arreˆts rendus au cours des quatre dernie`res de cennies, l’auteur explique trois the`mes qui sous-tendent la jurisprudence sur les chevauchements entre les brevets et la protection des obtentions ve ge tales. La majorite et les opinions dissidentes conside`rent les chevauchements en ce qui concerne l’ade quation des mesures incitatives, du risque d’incohe rence et de la logique historique de la re daction le gislative. Ces conside rations peuvent de terminer le re sultat d’affaires futures dans lesquelles le chevauchement de la protection est en cause.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.585
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.001

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 head0.151
GPT teacher head0.204
Teacher spread0.053 · 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.

Study designTheoretical or conceptual
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
Published2018
Admission routes2
Has abstractyes

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Same venueSSRN Electronic JournalSame topicIntellectual Property and PatentsFrench-language works237,207