Bridging, Braiding, and Weaving Indigenous and Western Science to Understand and Make Predictions About Weather and Climate Change
Bibliographic record
Abstract
ABSTRACT In this study, Indigenous Knowledge Holders were gathered in a collaborative forum to discuss weather, in particular, Indigenous Science knowledge related to severe weather, climate change, and weather prediction methods. Weather forecasting methods that arose in discussion were further explored based on a framework of Bridging , Braiding , and Weaving knowledge systems. Numerous Indigenous Science‐based weather prediction methods were documented, with most lending themselves to follow‐up testing. The Knowledge Holders emphasized that Indigenous Science is an important component within the broader domain of Indigenous Traditional Knowledge, with current and historic empirical and holistic insights into natural phenomena. There was consensus among the Knowledge Holders that Indigenous Science should not be perceived as subordinate to Western methodologies but instead viewed as a complementary and equivalent knowledge system. This paper underscores the importance of a co‐produced approach to research where Indigenous Knowledge Holders share their expertise to enrich and inform Western research design, including the interpretation of study results and the implementation of subsequent policy and decision‐making. We provide examples of Bridging , Braiding , and Weaving Indigenous Science using weather‐related phenomena, such as animal behavior, atmospheric patterns, and plant growth indicators, while also offering insights into the impacts of climate change and severe weather.
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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 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.007 | 0.025 |
| Scholarly communication | 0.009 | 0.013 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.002 | 0.003 |
| 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".