Two-Eyed Seeing in Research and its Absence in Policy: Little Saskatchewan First Nation Elders' Experiences of the 2011 Flood and Forced Displacement
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
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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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.007 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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