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Record W7099240143

Use of Traditional Knowledge for University Research: Conflicts Between Research Ethics and Intellectual Property Ownership Policies

2015· article· en· W7099240143 on OpenAlexaboutno aff

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicIndigenous Knowledge Systems and Agriculture
Canadian institutionsnot available
Fundersnot available
KeywordsIntellectual propertyTraditional knowledgePromotion (chess)State (computer science)Value (mathematics)Research ethics
DOInot available

Abstract

fetched live from OpenAlex

Abstract: The cultural knowledge of Aboriginal communities has been a long-time interest of social scien-tists. More recently, researchers from natural sciences and biotechnology have also taken an interest in “traditional knowledge”, for example, knowledge about medicinal plants. Research that involves the cultural knowledge of Aboriginal communities — especially that with perceived commercial value — is demanding an understanding on the part of all university researchers and university administrations of both ethical and legal issues, as well as how research ethics and intellectual property ownership policies are interrelated. While research ethics and intellectual property ownership policies in Canada are both evolving to meet the demands of new and complex situations, a key question is whether they are evolving in isola-tion of one another — and if so, where continued divergent evolution leads. This paper examines the cur-rent state of ethical research guidelines and intellectual property ownership policies at universities in British Columbia, and identifies how these policies may foster, impede, or channel the protection and promotion of traditional knowledge.

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)0.004
metaresearch head score (Gemma)0.002
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: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.452
Threshold uncertainty score0.672

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.820
GPT teacher head0.404
Teacher spread0.416 · 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

Citations0
Published2015
Admission routes1
Has abstractyes

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