Bibliographic record
Abstract
Geographers and others have written many words about British Columbian environmental politics. Stories about this place often revolve around conflicts between the government, the forest industry, First Nations, and environmentalists, battling it out to secure their vision of appropriate land use on the ground. This paper examines a particularly heated conflict over land use in the Great Bear Rainforest region, a large tract of temperate rainforest blanketing the central and north coasts of British Columbia. But this essay takes a different cut into understanding this particular political event, in that it tracks an often-unrecognized actor through the politics there: the grizzly bear. Drawing inspiration from scholarship that challenges the primacy of humans in our understandings of politics and social life, I argue that the grizzly bear influences and inflects BC's coastal forest politics; it is an important player in the transformation of the Great Bear Rainforest. I tell the story of environmental politics there by tracing the grizzly bear's shifting relationships with others, including with settlers, conservation biologists, environmentalists and money, all of which are consequential for the grizzly bear, and for others in the region.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.012 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".