Developing Participation and Understanding Through Community Engagement. Engaging with the Kitsumkalum Land Code Policy
Bibliographic record
Abstract
Kitsumkalum Nation is an Indigenous community located in Northwest British Columbia, Canada. They are working to redefine their relationship with the Canadian Government by pursuing a major policy change through Land Code. Kitsumkalum Nation realized that they needed to undertake community engagement strategies about the proposed Land Code policy change, with the goals of increasing community awareness of this complex technical issue and securing First Nations’ input into the decision-making process. This research, designed to contribute to the scholarly literature on community engagement processes, was based on the expressed desire of the Kitsumkalum Nation to determine the best way to communicate with community members. \nAfter several meetings with Kitsumkalum Nation staff, I conducted a literature review on community engagement. As a result of my preliminary research on this topic and extensive consultations with community leaders, the Kitsumkalum Nation decided to experiment with video communications as a method to share information more efficiently and to engage the community in discussions and decision making. Typical methods of communicating information to Kitsumkalum Band members, such as public meetings, have not met the Nation’s needs or expectations. They hoped that the shortcomings in earlier communications methods may be overcome in part through the use of video communications as a community engagement tool.
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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 | 0.015 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.012 | 0.010 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.001 | 0.014 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".