A Culturally Grounded Method for Dialogue Between Indigenous Peoples and Researchers on Emerging Technologies: Lessons from the Gene Drive Context
Bibliographic record
Abstract
This paper presents a participatory method for conducing a collaborative and culturally appropriate dialogue process between gene drive researchers and Indigenous Peoples and local communities. Coordinated by the Outreach Network for Gene Drive Research and representatives of the International Indigenous Forum on Biodiversity (IIFB), this dialogue aimed to build trust and facilitate mutual understanding and create a safe space for sharing traditional knowledge, rather than to reach decisions on the research or implementation of gene drive technology. Over a three-year period, the dialogue evolved through multiple formats, recognising the specific needs to establish a meaningful and culturally appropriate dialogue between these two groups, while ensuring that Indigenous Peoples and local communities could share their traditional knowledge, traditions and innovations in a safe and trusted environment. The method integrates key engagement principles - such as good faith, reciprocity, inclusivity, and respect for Indigenous Peoples and local communities' knowledge systems - and describes how they were operationalised in practice. It provides a concrete example of applied engagement methodology in the context of gene drive and explores how these principles have influenced the dialogue's format and the journey of both groups throughout this process, while also sharing some of the challenges they encountered. This is not a theoretical review, but a joint account from practitioners from diverse backgrounds and interests, on how engagement methods can be implemented in real-world settings. The approach offers practical insights for designing sustained and scalable engagement strategies between scientists and Indigenous Peoples on complex and emerging science topics.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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 teacher head, 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".