Aboriginal antidiabetic plant project with the James Bay Cree of Québec
Bibliographic record
Abstract
Purpose Research projects involving traditional knowledge are finding new ways of dealing with intellectual property rights and commercialisation. Influenced by calls for fair and equitable protocols involving access and benefit sharing regimes, researchers are developing new standards of practice. Here this paper aims to explore the process by which the CIHR Team in Aboriginal Antidiabetic Medicine (TAAM) came to address these issues within the scope of participatory action research. Design/methodology/approach A case study method is applied in order to highlight key events and topics. The legally binding research agreement developed for this project is used to illustrate examples of how the needs of First Nations stakeholders and of researchers are met. Findings The paper finds that strong research partnerships are characterized by accountability, adaptability, transparency, good and frequent communication and ultimately, trust. Researchers should be prepared to take a more “human” approach in their studies as the establishment of personal relationships are as important as the research itself. Proposals should include both monetary and intangible outcomes where possible, which reflect aboriginal culture and decision. Practical implications This paper can help others to understand the needs of aboriginal peoples with regard to research. It also provides links to protocols and the legal research agreement used by TAAM that can serve as an adaptable template for future work. Originality/value Publicising the research agreement and experiences herein is meant to contribute to a body of knowledge that will one day lead to new research norms when dealing with aboriginal peoples and traditional knowledge.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.029 | 0.002 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".