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Record W2253924128 · doi:10.4337/9781781955901.00035

Traditional knowledge governance challenges in Canada

2015· book-chapter· en· W2253924128 on OpenAlexaffabout
Jeremy de Beer, Daniel Dylan

Bibliographic record

VenueEdward Elgar Publishing eBooks · 2015
Typebook-chapter
Languageen
FieldHealth Professions
TopicIndigenous Studies and Ecology
Canadian institutionsLakehead UniversityUniversity of Ottawa
Fundersnot available
KeywordsCorporate governanceBusinessPolitical scienceFinance

Abstract

fetched live from OpenAlex

This book chapter canvasses the fragmented nature of jurisdiction over traditional knowledge in Canada. It relates traditional knowledge governance issues to the operations of Canadian federalism and relationships among Aboriginal Peoples and Canada’s federal, provincial and territorial governments. The authors discuss the conceptual nature of traditional knowledge, identify practical challenges associated with its protection, investigate legal jurisdictional issues with implementing legislation or other measures protecting traditional knowledge, and inventory policy initiatives to address traditional knowledge. They propose that federal, provincial and territorial governments can only meaningful engage with Aboriginal Peoples about traditional knowledge if these governments themselves engage in a process of collaborative or cooperative federalism. Doing so is one step toward the fulfilling these governments’ duty to not just consult but negotiate with Aboriginal Peoples toward treaties that govern rights to traditional knowledge that are consistent with Canada’s international obligations under Article 31 of the United Nations Declaration of Rights on Indigenous Peoples and other instruments.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.373
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.003
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 head0.195
GPT teacher head0.320
Teacher spread0.125 · 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 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
Published2015
Admission routes2
Has abstractyes

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