Bibliographic record
Abstract
In October 1993, 130 representatives from twenty-six countries met in Toronto, Canada, to inaugurate a governance regime designed to protect the world's forests. Participants agreed on ten principles for sustainable forest management, from controlling harvests to ensure steady timber yields over time while protecting fragile ecosystems, to protecting the rights of local forest-dwellers. The implementation of these principles would not be cost-free, and monitoring compliance hard to achieve. Nonetheless, participants agreed that this program represented a significant step forward in global forest conservation, while still allowing forest owners to benefit economically. This governance institution – the Forest Stewardship Council (FSC) – now covers 67 million hectares of forest across sixty-five countries. In many ways, it looks something like the treaty regimes we have examined in previous chapters. But, in many more ways, it is critically different. First, none of the participants at the Toronto meeting were government representatives. Instead, the driving force behind the establishment of the FSC was a coalition of NGOs, forest owners and timber companies, and forest-dwelling communities, led by the World Wildlife Fund (WWF), a leading international NGO. Second, the FSC achieves its goals through the transmission of information and the power of the market. If a timber-producing firm signs up to its standards, it agrees to allow an independent auditor to certify its compliance with FSC principles.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.021 | 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".