Two-Eyed Seeing: Seeking Indigenous Knowledge to strengthen climate change adaptation planning in public health
Bibliographic record
Abstract
Indigenous Peoples of Turtle Island have intimate knowledge of the environment and a long history of adapting to a changing climate. Yet, a scoping review of the literature on climate change adaptation measures identified only one document that provided an Indigenous perspective. On reflection, this pointed to a systemic issue in public health practice. To fill the gap, Cambium Indigenous Professional Services was retained to provide an Indigenous perspective. This paper highlights some of the lessons learned from this experience, not only when it comes to climate change, but also when addressing the broader social and environmental determinants of health. It presents factors public health authorities must consider to meaningfully engage with Indigenous Peoples and reduce health inequities. Significant and purposeful relationships will be developed when public health practitioners take the time to build trust, learn the history of Indigenous Peoples and embrace decolonization. This allows the creation of an ethical space where “Two-Eyed Seeing” can weave the different streams of evidence when developing and implementing climate change adaptation policies and programs.
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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 | 0.042 | 0.049 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.006 | 0.011 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".