Science-to-conservation disconnections in Borneo and British Columbia
Bibliographic record
Abstract
Borneo differs fundamentally from Canada, but reflections on the struggles to improve the fates of its tropical rain forests may resonate with people engaged in the same struggles on the other side of the Pacific. I frame these reflections around the question of why my efforts as a researcher in Borneo failed to cause a change from predatory logging of old growth to conservation through improved forest management. Perhaps my most fundamental mistake was unwillingness to recognize the immense profitability of forest liquidation through multiple-premature re-entry logging, especially when followed by conversion to plantations of African oil palm or Australian acacias. Superimposed on the high opportunity costs of conservation were governance failures that diminished the effectiveness of policies set by government as well as those set by certifiers of responsible management. Conservation of the mostly remote, flooded, and steep hinterlands still covered by forest will benefit from acknowledgment of the internationally recognized intrinsic land rights of Borneo’s indigenous peoples combined with full economic cost accounting of the consequences of forest degradation and conversion. Given the global importance of old growth in Borneo, Canada, and elsewhere, global funding for conservation should be made available with safeguards such as UNESCO Biosphere designations.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.018 | 0.009 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".