The Metagovernance of Climate Change: Institutional Adaptation to the Mountain Pine Beetle Epidemic in British Columbia
Bibliographic record
Abstract
The interior region of British Columbia is experiencing the most extensive mountain pine beetle outbreak ever recorded in North America, with 9.2 million hectares of red-attach pine forest identified in 2006. The epidemic is attributed to changing climate conditions and forest management policies that have resulted in a large number of mature, even-aged pine trees. Owing to the dominance of the forest industry in this region, there is widespread recognition that the mountain pine beetle outbreak will have significant socioeconomic impacts on forest-based communities in British Columbia. This paper examines the adaptation strategies of several communities that are affected by the mountain pine beetle epidemic. First, drawing from household survey research, latent levels of institutional apacity are discussed. Second, recent institutional adaptations are examined through the emergence of regional-scale beetle action coalitions. These institutional innovations can be characterized as a form of metagovernance, whereby collaboration and negotiated decision making are realized in the context of bureaucratic hierarchy and the extension of state power. The paper concludes with a call for stronger linkages between public and private sectors, as well as more robust forms of civic engagement as the basis for collective response to the mountain pine beetle outbreak.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".