Mapping the Patterns of Underestimated Researcher-Indigenous Collaboration
Bibliographic record
Abstract
This chapter focuses on the role and the contributions of Indigenous peoples and researchers towards the implementation of access and benefit-sharing (ABS). While States are often defined as the most competent authorities for the adoption of ABS implementation measures, the role and responsibilities of providers and users are often underestimated. As this chapter will show, the expectations imposed on states are exaggerated. They are not consistent with many sources, including relevant international instruments, research ethics, and the claims, experiences and practices of Indigenous and local communities themselves. Researchers and Indigenous peoples share common objectives when they are jointly and directly involved in research projects on genetic resources and traditional knowledge. They are interested and involved in regulating the circulation of genetic resources and traditional knowledge. As this chapter will illustrate, in countries like Canada researchers and Indigenous people have seized the opportunity to improve their relationships and work together. In fact, they have developed and mobilized several types of tools such as code of ethics and contracts to try to move towards more respectful and equitable relationships, including ABS.
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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 | 0.033 | 0.054 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.010 |
| Science and technology studies | 0.013 | 0.012 |
| Scholarly communication | 0.012 | 0.012 |
| Open science | 0.003 | 0.020 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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.
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".