Institutional thickening and innovation: reflections on the remapping of the Great Bear Rainforest
Bibliographic record
Abstract
As a response to forest conflict, contemporary remapping refers to re‐evaluations of resource values, new and diverse forms of governance among stakeholders, and compromises within patterns of land use that give greater emphasis to environmental and cultural priorities. This paper elaborates the processes of remapping by examining the role of institutional innovation in conflict resolution, with particular reference to the iconic Great Bear Rainforest of British Columbia. After years of conflict and protest, peace in the Great Bear Rainforest was heralded by an interim agreement in 2006, with final ratification likely in 2016. Conceptually, a four‐legged stakeholder model identifies the main institutional interests and their interactions through learning and bargaining. New forms of governance were created to bring the stakeholders together in constructive dialogue and then to reach and implement acceptable bargains. Analytically, the paper examines how this agreement has worked in practice by reflecting on the emergence of novel institutions that integrate the interests of key stakeholders. The discussion identifies six bilateral negotiations between: industrial and environmental interests; federal and provincial governments and aboriginal peoples; government and environmental interests; government and industry; industry and aboriginal peoples; and environmental groups and local communities. The remapping process has produced a thickening architecture of institutions that remain experimental even as they seek to promote sustainability, resilience and legitimacy.
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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 | 0.001 | 0.002 |
| 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".