Two-Eyed Seeing in Research and its Absence in Policy: Little Saskatchewan First Nation Elders' Experiences of the 2011 Flood and Forced Displacement
Bibliographic record
Abstract
Two-eyed seeing is a guiding framework for research that values and uses Indigenous and Western ways of knowing. In this article, we describe the merits and challenges of using two-eyed seeing to guide a collaborative research project with a First Nation community in Manitoba, Canada devastated by a human-made flood. In 2011, provincial government officials flooded 17 First Nation communities including Little Saskatchewan First Nation (LSFN), displacing thousands of people. To date, approximately 350 LSFN’s on-reserve members remain displaced. Two-eyed seeing ensured that the study was community-driven and facilitated a more thorough analysis of the data. This case study illuminated the absence of two-eyed seeing in policy making and decision making. We argue for the need to incorporate two-eyed seeing in policy making and program development, and to value and foster Indigenous perspectives in decision making within communities, especially regarding activities that have a direct impact on environments within or surrounding Indigenous lands.
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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 | 0.038 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.040 | 0.047 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.003 | 0.022 |
| Research integrity | 0.004 | 0.011 |
| Insufficient payload (model declined to judge) | 0.004 | 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".