Two‐Eyed Seeing as a strategic dichotomy for decolonial nursing knowledge development and practice
Bibliographic record
Abstract
The profession of nursing has recognized the need for contextual and relational frameworks to inform knowledge development. Two-Eyed Seeing is a framework developed by Mi'kmaw Elders to respectfully engage with Indigenous and non-Indigenous knowledges. Some scholars and practitioners, however, are concerned that Two-Eyed Seeing re-instantiates dichotomized notions regarding Western and Indigenous knowledges. As dichotomies and binaries are often viewed as polarizing devices for nursing knowledge development, this paper explores the local worldviews in which Two-Eyed Seeing emerged, proposing that the onto-epistemological and axiological 'roots' of the framework are antithetical to divisiveness, paradoxically asserting space for the dichotomy to stand. Two-Eyed Seeing, if understood as a relational, decolonial praxis, could fundamentally change the way nursing scholarship and practice operate by facilitating space for diverse knowledges, ways of being, doing and relating. In this paper, considerations for nursing scholarship and practice, as well as recommendations to support the uptake of Two-Eyed Seeing are explored. The authors assert that conceptual divisiveness, dichotomization and exclusion can be mitigated if nursing is informed by contextual knowledge, seeks to enact accountable partnerships with Indigenous knowledge holders, and holds the Mi'kmaq worldview upon which the concept developed in positive regard.
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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 | 0.030 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.012 | 0.080 |
| Scholarly communication | 0.018 | 0.019 |
| Open science | 0.002 | 0.027 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.004 | 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".