Using Two-Eyed Seeing in Research With Indigenous People: An Integrative Review
Bibliographic record
Abstract
Background: The Two-Eyed Seeing approach has been advocated for use in research with Indigenous people as it creates a space for Western and Indigenous ways of knowing to come together using the best of both worldviews to aid understanding and solve problems. Foundational literature presents its use as a promising way to promote ethical exchanges between Indigenous and non-Indigenous people, but the practical application of its concepts to research remains vague. Method: This integrative review, using the Whittemore and Knafl approach, describes the state of the literature pertaining to the interpretation and application of Two-Eyed Seeing. Following a search of the literature, 37 articles were selected for inclusion, and primary studies ( n = 11) were critiqued for quality. Data were extracted, analyzed, and synthesized into themes. Results: Three themes were compiled from the literature including (a) defining characteristics of Two-Eyed Seeing, (b) suggested attributes of those engaging with Two-Eyed Seeing, and (c) the application of Two-Eyed Seeing in research. Conclusions: This review demonstrates inconsistencies in how to date researchers have interpreted and applied Two-Eyed Seeing in research with Indigenous people. The collection of key attributes of researchers and application procedures to research discussed in this review present a new standard for the application of Two-Eyed Seeing to research with Indigenous people. Researchers using Two-Eyed Seeing should thoroughly describe their application of its concepts to promote its maturation into a well-defined framework for research with Indigenous people.
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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 | 0.029 | 0.067 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.014 | 0.014 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".