Environmental Bargains: Power Struggles and Decision Making over British Columbia's and Tasmania's Old-Growth Forests
Bibliographic record
Abstract
Over the past few decades, conflicts over resources have increased in scale and intensity. They are frequently dominated by environmental nongovernmental organizations (ENGOs) that fight, boycott, lobby, and negotiate with other interest groups to privilege nonindustrial, particularly environmental, values of resources. This article proposes an environmental bargaining framework to analyze the many and varied forms of interactions and processes through which ENGOs seek to change existing practices and decision structures. Drawing on political economy and political ecology approaches, environmental bargaining recognizes the importance of multiple perspectives, strategies of actors, and the regional context. Conceptually, the article interprets environmental conflicts along two dimensions: the distribution of power between actors and forms of interaction ranging from confrontational to collaborative. Examples from British Columbia, Canada, and Tasmania, Australia, reveal the value of comparative perspectives and the importance of the regional context that determines behavior and relationships between actors. While confrontational action has brought considerable change to Tasmania's forests, the example from British Columbia suggests that collaborative forms of decision making that are based on a balance of power have more potential to protect environmental values and bring peace to the woods.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.015 | 0.012 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".