Learning from past collaborative experiences: setting a pathway for natural sciences and engineering researchers to support Indigenous-driven aims
Bibliographic record
Abstract
In efforts to contribute towards reconciliation, some researchers have shown increased interest in collaborative work with Indigenous Peoples. However, those in technical fields, such as the natural sciences and engineering, are not traditionally trained in how to carry out Indigenous-driven research. This study learned from the successes and challenges of past technical research collaborations to better understand how these researchers can act as stronger allies. Qualitative interviews were carried out with five members of Indigenous communities and 35 researchers. The results showed diverse experiences and the need for more collaborative frameworks and supportive institutional environments within the natural sciences and engineering. Findings highlighted the wide range of issues to be considered in such work, grouped into (1) assessing personal preparation and mindset; (2) building and maintaining relationships; (3) community-aligned benefit; (4) practical and financial considerations; and (5) knowledge sharing and communication. In addition, participants identified institutional-level factors that could help (e.g., mentorship) or hinder (e.g., current recognition structures in many technical fields) efforts to carry out Indigenous-driven technical research. These results may stimulate and contribute to necessary work in the natural sciences and engineering on processes for equitable and thoughtful engagement with members of Indigenous communities to support Indigenous-driven research.
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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 | 0.071 | 0.081 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.035 | 0.021 |
| Scholarly communication | 0.019 | 0.025 |
| Open science | 0.006 | 0.051 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.006 | 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".