“These Trees Have Stories to Tell” Linking Denésƍliné Knowledge and Dendroecology in the Monitoring of Barren-ground Caribou Movements in the Northwest Territories, Canada
Bibliographic record
Abstract
Grounded in an Indigenous methodological framework and using dendroecology as a scientific assessment tool in combination with oral history analysis, this thesis assesses changes to caribou movement patterns in the traditional territory of Lutsel K’e Dene First Nation (LKDFN), Northwest Territories, Canada. This approach was used to explore ways in which scientific methods can be used within an Indigenous research framework. This approach shows that Indigenous ways of knowing can set the basis for identifying the important research questions and methods, and that appropriate and complimentary scientific methods can be used to build upon that framework. I draw from methods of natural and social science disciplines including Participatory Action Research (PAR), ethnography, community-based research, participant observation, and dendroecology (tree-ring analysis). I worked with elders and harvesters to document oral histories about caribou movement patterns and augmented their observations and stories with information from dendroecological assessment techniques. This thesis provides a framework for those seeking to conduct ecological research by drawing linkages between Indigenous knowledge systems and scientific methods. I use the specific example of broadening our understanding of caribou movements by combing oral history narratives and dendroecology, however, the lessons learned could be applied across a wide range of disciplines. This research project is not only about asking questions related to the impacts of resource development to the community of Lutsel K’e and the caribou on which they depend, it also demonstrates that Indigenous communities can embrace and implement scientific methodologies while remaining grounded in our own Indigenous knowledge systems and practices.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.010 | 0.008 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".