Sharing What we Know About Living a Good Life: Indigenous Approaches to Knowledge Translation
Bibliographic record
Abstract
Knowledge Translation (KT), a core priority in Canadian health research, policy, and practice for the past decade, has a long and rich tradition within Indigenous communities. In Indigenous knowledge systems the processes of "knowing" and "doing" are often intertwined and indistinguishable. However, dominant KT models in health science do not typically recognize Indigenous knowledge conceptualizations, sharing systems, or protocols and will likely fall short in Indigenous contexts. There is a need to move towards KT theory and practice that embraces diverse understandings of knowledge and that recognizes, respects, and builds on pre-existing knowledge systems. This will not only result in better processes and outcomes for Indigenous communities, it will also provide rich learning for mainstream KT scholarship and practice. As professionals deeply engaged in KT work, health librarians are uniquely positioned to support the development and implementation of Indigenous KT. This article provides information that will enhance the ability of readers from diverse backgrounds to promote and support Indigenous KT efforts, including an introduction to Indigenous knowledge conceptualizations and knowledge systems; key contextual issues to consider in planning, implementing, or evaluating KT in Indigenous settings; and contemporary examples of Indigenous KT in action. The authors pose critical reflection questions throughout the article that encourage readers to connect the content with their own practices and underlying knowledge assumptions.
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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 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.037 | 0.103 |
| Scholarly communication | 0.023 | 0.024 |
| Open science | 0.005 | 0.023 |
| Research integrity | 0.007 | 0.013 |
| 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".