Using two-eyed seeing to bridge Western science and Indigenous knowledge systems and understand long-term change in the Saskatchewan River Delta, Canada
Bibliographic record
Abstract
Although researchers now recognize that Indigenous knowledge can strengthen environmental planning and assessment, little research has empirically demonstrated how to bring together Indigenous knowledge and Western science to form a more complete picture of social-ecological change. This study attempts to fill this gap by using ‘two-eyed seeing’ – an approach that brings together Indigenous and Western perspectives on an equal basis – to collect and analyze changes in the Saskatchewan River Delta since upstream dams were built in the 1960s. Results found corroboration across the knowledge systems that operation of dams has lowered summer flows and created unnaturally high winter flows. The knowledge systems, however, diverged in some areas, such as the production of northern pike, where local residents observed abundant pike but records showed the pike commercial harvest declining to near zero. Indigenous knowledge alone provided information about berries and berry seasons. This two-eyed seeing approach can enhance environmental assessment and planning by providing a more accurate and coherent narrative of long-term social-ecological change.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".