Two-Eyed Seeing Application in Research Analysis: An Integrative Review
Bibliographic record
Abstract
Background Research approaches used to understand Indigenous people in Canada have predominately used Western approaches and have not reflected Indigenous ways of knowing, protocols, or worldviews. The landscape of research involving Indigenous people is changing with a growing number of Indigenous scholars reclaiming and revitalizing Indigenous knowledge. Two-Eyed Seeing provides a framework whereby conscious and deliberate conversations and research approaches are determined prior to and throughout the research process by the research team to guide a balance of each knowledge. However, Two-Eyed Seeing approaches or methods used during data analysis within the literature remains sparse necessitating this integrative review. Purpose To review the literature where Two-Eyed Seeing has been applied and to identify the approaches or methods used during data analysis and discuss the implications for research with Indigenous communities worldwide. Methods The five-stage approach outlined by Whittemore and Knafl (2005) was used to guide this integrative review. Results A total of 321 articles were reviewed from four databases, yielding 32 articles. Conclusions This integrative review is novel in that it is the first review known to specifically explore the use and application of a Two-Eyed Seeing framework during data analysis. Five main themes are presented including (1) Indigenous community member involvement with analysis, (2) Co-Learning during data analysis, (3) Visual or symbolic conceptualizations to guide analysis, (4) Statement acknowledging Indigenous knowledge during data analysis, and (5) Sharing of Traditional stories to guide data analysis. Additionally, five Two-Eyed Seeing approaches less commonly used during data analysis are provided.
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 | 0.057 | 0.133 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.031 | 0.028 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.013 | 0.013 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".