Weathering Changes: Cultivating Local and Traditional Knowledge of Environmental Change in Tr'ondëk Hwëchin Traditional Territory
Bibliographic record
Abstract
This paper explores a particular experience of cultural bridging between the Heritage Department of the Tr’ondëk Hwëch’in (TH) First Nation and academics and government funders taking part in the 2007–09 International Polar Year. The TH Heritage Department acted as lead researcher on the project entitled Documenting Traditional Knowledge in Relation to Climate Change. TH Heritage staff spearheaded and largely carried out the project work. Academic researchers, acting as contractors, collaborated in some project activities and produced academic papers summarizing the work. This collaboration provided a rare opportunity for the TH Heritage Department to share the research it has conducted for more than a decade in the broader, institutional context of university and government research. Its success highlights the fact that relationships between these partners are evolving and becoming more equitable: First Nations research is receiving more support, and the corpus of mainstream knowledge is changing, allowing different bodies of work to “count” as knowledge. This paper analyzes some of the differences between TH Heritage approaches to its mandate for gathering and sharing Traditional Knowledge (TK) and the understandings and uses of TK by other governments and by university-based academics. On the basis of project results and recent policy developments in northern governance and research, it makes practical recommendations for reconciling knowledge approaches and building mutually supportive research relationships between First Nations, academics, and government.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.008 |
| Scholarly communication | 0.003 | 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".