Seasons of research with/by/as the Keweenaw Bay Indian Community
Bibliographic record
Abstract
In response to generations of inequitable research to/for Indigenous communities, many have and are developing research practices that center Indigenous priorities. In this paper, we share the Seasons of Research framework developed by the Keweenaw Bay Indian Community and University collaborators. First, we outline the scholarship that provides the foundations for research and being researchers in Keweenaw Bay. This section includes a comprehensive table that summarizes resources for building, strengthening, and sustaining equitable research partnerships with/by/as Indigenous communities. Next, we share the guidance for research partnerships we created together that uses the Medicine Wheel to illustrate an interconnected system of partnership teachings. The guidance aims for balance between and among four seasons of research: relationship building, planning and prioritization, knowledge exchange, and synthesis and application. Research partnerships with/by/as the Community demonstrate respect for each other's differences, honor reciprocity in actions, exemplify responsibility for differing commitments, and express reverence for shared lands, waters, and living beings. Personal reflections by lead author Emily Shaw are shared to demonstrate the process and practices associated with seasons of research, bridging Indigenous wisdom, social and natural sciences, and environmental engineering. We conclude with a few words on the transformation of the research landscape with Indigenous peoples at home and abroad.
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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 | 0.027 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.032 | 0.023 |
| Scholarly communication | 0.016 | 0.009 |
| Open science | 0.003 | 0.022 |
| Research integrity | 0.002 | 0.008 |
| 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".