Community-based assessments of change, contributions of Inuvialuit knowledge to understanding climate change in the Canadian Arctic
Bibliographic record
Abstract
An emerging theme in climate research is bridging the gap between Western science and Inuit knowledge of climate change to better understand Arctic climate change. This thesis is encouragement for this theme. Based in part on the collaborative research project 'Inuit Observations of Climate Change' (1999-2000) in Sachs Harbour, Western Canadian Arctic, I describe how local, land-based expertise and community-based assessments can provide observations, predictions and explanations of climate change at scales and in contexts currently underrepresented in climate change research. Firsthand experience working with local experts and scientists is used as a basis for a conceptual framework that explains how to find common ground between Inuvialuit traditional knowledge and Western science. This framework includes five areas of convergence in which traditional knowledge can complement scientific approaches to understanding climate change in the Canadian Arctic. These areas are: the contributions of traditional knowledge (i) as local scale expertise; (ii) as a source of climate history and baseline data; (iii) in formulating research questions and hypotheses; (iv) as insight into impacts and adaptation in Arctic communities; and (v) for long term, community-based monitoring. (Abstract shortened by UMI.)
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 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".