Developing the Ka:’yu:’k’t’h’/Che:k’tles7et’h’ First Nations Stewardship Program
Bibliographic record
Abstract
Over the past two centuries, the combined impacts of resource extraction, over-harvesting, and government policy has adversely impacted environmental health within Ka:’yu:’k’t’h’/Che:k’tles7et’h’ First Nations’ (KCFN) territory, and impaired KCFN members’ ability to utilize resources to harvest foods and medicines, and for cultural and other purposes. In response, KCFN has created a Stewardship Program to monitor and safeguard ecological and cultural resources, to document and/or deter human activities, and to establish a presence in the territory. In the first year of the program, Stewardship staff acquired equipment, conducted training, and carried out an initial set of activities, including boat-based patrols of the territory, and baseline ecological monitoring. To ground program development in KCFN community values and priorities, KCFN is (1) engaging community members and leadership to determine the long-term program goals, to document the key issues and concerns the program should address, and to identify core monitoring activities; (2) developing tailored monitoring and analysis plans to meet their data and decision-making needs; and (3) developing a custom data management system for data storage, visualization, analysis, and reporting. We review the process KCFN is undertaking to develop the Stewardship Program and highlight initial insights from the program’s first year.
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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 | 0.008 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".