Knowledge coevolution: generating new understanding through bridging and strengthening distinct knowledge systems and empowering local knowledge holders
Bibliographic record
Abstract
Abstract The effective and appropriate bridging of Western science with traditional or Indigenous knowledge is an ongoing discussion in the literature and in practice. The discourse transitioned from separate knowledge system to knowledge integration and most recently to knowledge co-production. We argue it is the moral and ethical responsibility of Western scientists working in and with Indigenous communities to make a concerted effort to collectively create mutually advantageous new knowledge while strengthening traditional knowledge and considering the normative impacts of Western science methods. Our knowledge coevolution framework provides guidance for achieving this in a flexible manner that can be applied to an array of research programs. Project governance structure, steps for implementation, checks and balances, and challenges are presented within the context of research project execution. We then illustrate application of the model throughout a harvest study conducted in Gjoa Haven, Nunavut, Canada.
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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 | 0.018 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.007 | 0.033 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.002 | 0.021 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".