Decolonizing Action Research through Two-Eyed Seeing: The Indigenous Quality Assurance Project
Bibliographic record
Abstract
Action Research (AR) has been widely utilized in Indigenous contexts because of its emphasis on social transformation and synergies with Indigenous research approaches. Yet, while AR is seen as an attractive option for working in Indigenous research contexts, additional efforts are needed to ensure that AR adequately interrogates collaborations between Western and Indigenous knowledge systems. The application of the principle of two-eyed seeing (TES), which refers to the process of seeing from the strengths of Indigenous ways of knowing with one eye while using the other eye to see with the strengths of Western ways of knowing (Bartlett, Marshall, & Marshall, 2012), can center decolonial goals, addressing the shortcomings of AR. This article describes the operationalization of TES through the Indigenous Quality Assurance Project, focusing on the four key essentials of TES: co-learning, knowledge scrutinization, knowledge validation, and knowledge gardening (Bartlett, 2017).
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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 | 0.192 | 0.131 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.011 | 0.037 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.005 | 0.039 |
| Research integrity | 0.004 | 0.010 |
| 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".