Environmental Bargaining and Boundary Organizations: Remapping British Columbia's Great Bear Rainforest
Bibliographic record
Abstract
In recent decades, the creation of conservation areas has been a significant and contested trend in resource peripheries around the globe, embracing the “remapping” of resource extents, tenures, and values and thereby land use patterns and regional development trajectories. Environmental nongovernmental organizations (ENGOs) have emerged as key actors in the conflicts underlying this remapping, as advocates of environmental values and opponents of vested economic and political interests engaged in large-scale resource commodification. Remapping is contentious because it is inescapably normative, rendering moral judgments and alterations of property rights and the meaning of sustainable development. The outcomes of remapping are highly contingent, driven by environmental bargaining processes that describe the formal and informal interactions among ENGOs, industrial interests, different levels of government, and other actors with conflicting interests, strategies, and alliances. This article explores how conflicts were resolved in the creation of the Great Bear Rainforest on British Columbia's central coast. Conceptually, the stakeholder model approach to resource conflict is elaborated by emphasizing the roles of ENGOs as advocates and representatives of environmental values within scientific boundary organizations created specifically to be key facilitators in the bargaining process. The study draws on forest policy documents, records of negotiation, surveys of the region's ecological and socioeconomic structures, and field visits. The analysis reveals the Coast Information Team as the multirepresentative scientific boundary organization that developed a shared, accepted multilayered geographic information system of the region. This map provided a “shared currency” and the basis for agreement regarding (1) land use zoning at multiple scales, (2) ecosystem-based management, and (3) conservation mapping.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".