Boundary spanners: a critical role for enduring collaborations between Indigenous communities and mainstream scientists
Bibliographic record
Abstract
The need to diversify science includes increasing both the diversity of science practitioners and the voices included in decision-making processes. Indigenous communities have been sought out to provide Indigenous knowledge to mainstream science research programs. As working across the mainstream science and community boundary is increasingly codified into the future of natural sciences, models for equitable collaboration and roles within project structures are needed. The goal of this project is to present a framework for collaboration between mainstream science and Indigenous communities. Specifically, we are addressing an under-recognized role central to partnership, a boundary spanner, who acts as the fulcrum facilitating collaboration. To better understand the role of boundary spanners in collaborative projects, we engaged six boundary spanners who participated in semi-structured interviews and workshops. Emergent common experiences and perspectives of how boundary spanners can be supported and their role in collaborative projects were defined and articulated. The boundary spanners identified 10 characteristics that contribute to equitable partnership between mainstream science and Indigenous communities. From the perspective of the boundary spanners, they detailed how collaborative projects can be structured to increase long-term partnerships and community support of research projects. Equitable collaboration between Indigenous communities and mainstream science is frequently only achieved when individuals at the interface of the mainstream science and Indigenous community have a high level of cultural competency. Equally important is the support provided to the boundary spanners and early engagement of partner Indigenous communities. Through the use of story and metaphor, we highlight the voices of boundary spanners and how their contributions can best be used.
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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 | 0.049 | 0.068 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.042 | 0.066 |
| Scholarly communication | 0.021 | 0.030 |
| Open science | 0.004 | 0.042 |
| Research integrity | 0.008 | 0.009 |
| 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".