Using Two-Eyed Seeing in Research With Indigenous People: An Integrative Review
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.048 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it