A Little lecture on the big burn: Bioenergy and the privatization of British Columbia's Crown forests
Bibliographic record
Abstract
The combination of a gutted B.C. Forest Service, vast areas of not sufficiently restocked forest lands, a quirky loophole in the Kyoto Protocol and a provincial government ideologically driven to sell off public assets has created the perfect opportunity to burn down B.C.'s forests in a biofuel boondoggle and the last barriers to privatization of B.C.'s Crown forests. Interviews conducted with over a dozen ex-government foresters, industry representatives, contract foresters, silviculturalists, forest-sector round-table participants and political representatives point to this new direction that government is taking Crown forests—with no public consultation and media, like the government, that are failing to serve public interests. The voices of the whistle blowers point to a colossal failure of imagination by government that has implications to forest health, climate change mitigation and adaptation, other public interests in Crown lands from public access to biodiversity and water quality, First Nation interests, and international credibility on carbon accounting and standards and on certification. The lecture will explore the dystopic picture of what is planned and an alternative vision for Crown forests that has been put forward by the critics as a world leader in ecosystem services and valuation. The lecture notes were taken from an original longer article entitled The Big Burn, first published by Focus Magazine in August of 2010. 2
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.027 | 0.002 |
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".