Evolving co-management practice: Developing a community-based environmental monitoring framework with Tl'azt'en nation on the John Prince Research Forest.
Bibliographic record
Abstract
This thesis describes a community-based research project that was conducted in partnership with Tl'azt'en Nation and the co-managed John Prince Research Forest. The purpose of the research was to identify, develop, and verify Tl'azt'en environmental measures for five traditional use activities: talo ha'hut'en - fishing salmon (Oncorhynchus spp.), huda ha'hut'en - hunting moose (Alces alces), tsa ha tsayilh sula - trapping beaver (Castor canadensis), duje hoonayin - picking huckleberries (Vaccinium membranaceum), and yoo ba ningwus hunult'o - gathering soapberries (Shepherdia canadensis) for medicinal use. The process of developing Aboriginal environmental measures was participatory and iterative. I worked in partnership with two teams of Tl'azt'en community members, including Elders and traditional land users. The central methods used in our framework included: focus groups, workshops, one-on-one interviews and Photovoice. Our participatory research approach was evaluated throughout the course of the project and comprehensively at the end of the project by Tl'azt'en team members, researchers, and research assistants. This iterative evaluation process fostered an adaptive outlook and ensured that our methodology was culturally appropriate and meaningful. Evaluation results revealed how participant satisfaction, personal development, independence, and the building of relationships contributed to sustained participation and the achievement of project objectives. Overall, 252 Tl'azt'en environmental measures were developed in this project for our five focal traditional use activities and two inductively identified environmental monitoring themes: monitoring environmental change across Tl'azt'en Nation traditional territory and monitoring community adherence to Tl'azt'enne traditional environmental land use methods and principles. A prioritized subset of these measures will be applied in the future through a Tl'azt'en community-based environmental monitoring initiative on the John Prince Research Forest. Applying th
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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 | 0.035 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.024 | 0.026 |
| Scholarly communication | 0.013 | 0.011 |
| Open science | 0.005 | 0.016 |
| Research integrity | 0.003 | 0.005 |
| 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".