MétaCan
Menu
Back to cohort
Record W3158981886

Understanding African and Like-Minded Countries’ Positions at WIPO-IGC

2019· article· en· W3158981886 on OpenAlexaff
Chidi Oguamanam

Bibliographic record

VenueSSRN Electronic Journal · 2019
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicIntellectual Property and Patents
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsMandateIntellectual propertyContext (archaeology)NegotiationPolitical scienceSubject (documents)Law and economicsLawSociologyGeography
DOInot available

Abstract

fetched live from OpenAlex

Since 2001, World Intellectual Property Organization (WIPO) member states and regional blocs have been striving to agree on an international legal instrument(s) covering the effective protection of traditional knowledge (TK), traditional cultural expressions (TCEs), and genetic resources (GRs). They have been conducting this task pursuant to the mandate of an expert committee of WIPO—the Intergovernmental Committee on Intellectual Property and Genetic Resources, Traditional Knowledge and Folklore (now known as traditional cultural expressions) (IGC). For nearly two decades of the committee’s work, progress has come rather slowly, leaving a lethargic haze not only over the African Group as a bloc, but also over the category of countries and regional groups broadly known as demandeur states. Demandeur states are heavily invested in demanding stronger protection of the subject matter covered by the IGC mandate. While the IGC’s work has provided policy and jurisprudential insights into the subject matter of its mandate, its prospects of ultimately delivering on its mandate remain unknown. Using the structure of the emerging tripartite instruments under negotiation, this Article seeks to shed light on the position of like-minded states with a bias for African Group activism on key negotiating interests and the overall dynamic in which those positions are advanced. This Article provides some international legal context for the IGC’s work and concludes by offering insights on African and like-minded countries’ strategy moving forward—whether within or outside of the IGC.

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)0.006
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.046

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0100.018
Scholarly communication0.0080.006
Open science0.0010.006
Research integrity0.0040.003
Insufficient payload (model declined to judge)0.0040.000

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 head0.078
GPT teacher head0.209
Teacher spread0.131 · 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 designQualitative
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
Published2019
Admission routes1
Has abstractyes

Explore more

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