Two-Eyed Seeing and developmental origins of health and disease studies with indigenous partners
Bibliographic record
Abstract
Globally, mortality of Indigenous persons is greater than that of their non-Indigenous counterparts, which has been shown to be disproportionately attributable to non-communicable diseases. The historically subordinate position that Indigenous Knowledge (IK) held in comparison to Western science has shifted over the last several decades, with the credibility and importance of IK now being internationally recognized. Herein, we examine how Marsahall's (2014) Two-Eyed Seeing can foster collaborative and culturally relevant Developmental Origins of Health and Disease (DOHaD) studies for health and well-being by using '..the best in Indigenous ways of knowing…[and] the best in Western (or mainstream) ways of knowing…and learn to use both these eyes for the benefit of all.' At its core, Two-Eyed Seeing also includes the principles of ownership, control, access and possession, and Community-Based Participatory Research, which further reinforces the critical role of Indigenous peoples taking active roles in DOHaD research. Additionally, we also present a partnership model for working with Indigenous communities that includes the principles of respect, equity and empowerment. As researchers begin to fill the gap in Indigenous health, we outline how Two-Eyed Seeing should form the basis of DOHaD studies involving Indigenous communities. This model can be used to develop and guide projects that result in robust and meaningful participatory partnerships that have impactful uptake of research findings.
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 | 0.042 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.017 | 0.016 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.002 | 0.026 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 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".