How to put forest and conservation genomics into motion for and with Indigenous communities?
Bibliographic record
Abstract
Sustainable management and conservation (SMC) projects for natural resources in collaboration with Indigenous Peoples using a genomics approach are increasing in number. Information and tools/applications derived from genomics can be useful to them, particularly in the context of climate change. However, the challenge of translating these applications into practice and harnessing them to serve Indigenous communities remains. We present an exploratory literature review that addresses: (1) the demonstrated utility of genomics in SMC projects involving Indigenous Peoples, (2) some issues that may limit the adoption of genomics tools, and (3) the collaborative work between researchers and Indigenous communities in the analyzed studies. The demonstrated uses identified were largely of a socioecological nature. The complementary nature of Indigenous knowledge and scientific knowledge in genomics was recognized as an opportunity that should be further developed to address current challenges such as climate change. Regarding the adoption into practice of this technology in SMC projects, in addition to similar issues with other end users, the integration of the needs, traditional values and knowledge of Indigenous communities in genomics projects also represents a challenge in the context of the decolonization of genomics research. Finally, community-researcher collaboration was identified as a key element in promoting the successful uptake of genomics in SMC.
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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 | 0.044 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.018 |
| Scholarly communication | 0.012 | 0.024 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.007 | 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".