DamXan gud.ad t’alang hllGang.gulXads Gina Tllgaay (Working together to make it a better world)
Bibliographic record
Abstract
This article is a compilation of my thoughts based on interviews with the (coastal) village residents of Skidegate, Haida Gwaii. I asked, How can we make our communities healthy and able to withstand the rising winds, waters, more extreme temperatures, and droughts, all of which are related to climate change? How can we use our ancient kilyahdas (spoken laws) to empower our Nation to uphold our values of Yahguudang (respect), Ista ad isgid (reciprocity), Gud ‘Laa (consensus), Tll’yahdah (make things right), and ’Laa guu ga kanhlln (stewardship) to create a safe, healthy planet, including the ocean, for present and future generations? Study participants identified the need for more education on climate change impacts and the reinvigoration of ancestral laws. Colonization is discussed throughout this research because of the impacts it has had and continues to have on our life ways. The removal of Canadian legislation, such as the Indian Act, Species at Risk Act, and Fisheries Act, and the revitalization of ancient laws lived for thousands of years, which taught the Kuuniisii (the ancestors) to live respectfully with all aspects of the earth, is needed. These ancient laws offer respect and interdependence, as well as control over our Nation and other nations collectively. Currently, Indigenous communities are facing ongoing colonization while attempting to address the impacts of climate change. Reinfusing our kil yahdas (spoken laws) and kuuya (precious things or values) is important for rebuilding and maintaining healthy and resilient communities and strong governance. We hope that this reanimation will reduce the impacts of climate change, especially on our ocean.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.223 | 0.061 |
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".