Maybe National Park: Consultation, Conservation, and Conflict in the Okanagan-Similkameen
Bibliographic record
Abstract
This article is about the process of making a national park reserve in the South Okanagan - Lower Similkameen region of British Columbia, as reflected in the perspectives of people who live within the community or who are connected to Parks Canada’s decision-making process. For all its local focus, this article rests upon extensive background research: research into the process of making national park nature in Canada; research into land and wildlife management practices; and research on indigenous epistemologies about nature, and the progression of white settler culture in the British Columbia. Still, this article makes its primary contribution through its focus on debates about the proposed national park reserve in the South Okanagan – Lower Similkameen Valley. The article rests on both historical research into the area and interviews with current residents of the valley. Its purpose is to determine how stakeholders feel connected to the land on which they live and how they think that a national park reserve would change their relationship with the land. Ultimately, the article sheds light on and helps to understand the attitudes and opinions towards land management held by stakeholders of those areas being targeted for federal conservation, as well as the conflicts and collusions between residents and Parks Canada policies during such processes.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.050 | 0.018 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".