The nature of protest: constructing the spaces of British Columbia’s rainforests
Bibliographic record
Abstract
This paper examines the representations of nature circulating in a Greenpeace anti-logging campaign in British Columbia, Canada. The effort to stop industrial logging in a region of the central coast named ‘the Great Bear Rainforest’ is presented as a case study through which nature’s social production can be glimpsed. Part of the larger ‘war in the woods’ that gripped British Columbia throughout the 1990s, the campaign considered here pitted Greenpeace and other environmental non-governmental organizations and their grassroots supporters against the forestry industry and many members of resource-producing communities. Through an analysis of campaign literature, newspaper coverage and ‘letters to the editor’, it is argued that the preservationist position advanced by Greenpeace visually and discursively constructs a concept of pristine nature which appeals to urban populations, employs a neocolonial representation of First Nations peoples and the nature within which they are situated, and finds authority and legitimacy in ecosystem discourse. Drawing both on work by Matthew Sparke concerning mapping and the narration of the nation and on Haripriya Rangan’s identification of regionality as a key concept in understanding nature’s production, it is suggested that the construction of nature considered in this case study needs to be understood as part of an articulation of a particular west coast metropolitan identity.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.031 | 0.030 |
| Scholarly communication | 0.014 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 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".