“What we see in the In-Between”: navigating ethics and equity in the role of leading research projects with Alaska Native communities
Bibliographic record
Abstract
As 13 leaders in research with Alaska Native communities, we came together in a workshop to self-define the role of boundary spanners within our cross-cultural contexts. We utilized convergence methods and participatory decision-making facilitation. Reflecting on chronic challenges and current issues of trying to do co-production of knowledge, our group discussed the boundary spanner role and how to create systemic change. We represented different career stages, gender identities, Indigenous and non-Indigenous peoples, ages, backgrounds, and job positions. We wrote this paper to illustrate positive and negative aspects of this role as framed in a typical career journey. The role is often not sustainable, includes a degree of conflict, and lacks support. We recognize that boundary spanners can act as enablers of boundaries. Healing is often interwoven with Indigenous and individual self-determination. Our workshop ended with the development of strategies to create systemic change through mentoring the next generation and addressing funding inequity and the cultural divide between communities and science/policy. A key concept from the workshop is the rejection of the term “boundary spanner”, because ideally, there should not be one individual doing the spanning duties, but everyone within the science/policy sphere working to dismantle boundaries.
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 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 | 0.110 | 0.070 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.062 | 0.074 |
| Scholarly communication | 0.027 | 0.020 |
| Open science | 0.005 | 0.039 |
| Research integrity | 0.009 | 0.014 |
| Insufficient payload (model declined to judge) | 0.004 | 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".