The “One Voice” method: Connecting Inuit Qaujimajatuqangit with western science to monitor Northern Canada’s freshwater aquatic environment
Bibliographic record
Abstract
Scientific methods used to assess environmental changes in the Arctic are well established. However, researchers face vast, inaccessible expanses with a harsh climate. This presents logistic and financial challenges few other places in Canada experience. Community based monitoring may improve our ability to track changes in Canada’s North. A major barrier is that there is no set methodology to match observations made by Inuit while on the land with many scientific indicators. This project explored links between The Inuit Qaujimajatuqangit (IQ) and western science knowledge systems through a series of semi-directed interviews with knowledge holders led by a “curious scientist.” The focus was on specific water quality indicators. The interviews asked targeted questions to identify where these indicators and Inuit observations meet. Three interviews were held for each group of participants. The last set were held at sites known to have consistently high- or low-quality drinking water. Water at the interview sites was sampled while the last interview was conducted. This helped to identify a set of substances found in the water to use as common indicators that describe the aquatic environment. These common indicators connect the two knowledge systems. They can be used to establish baseline conditions and measure the impact of stressors. Their use may help refine aquatic monitoring programs to better address community concerns. The result will be a more holistic understanding of the aquatic environment using both knowledge systems.
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How this classification was reachedexpand
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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.006 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.002 |
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 teacher head, 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".