Biodiversity offsets, the Kunming-Montreal Global Biodiversity Framework, and the artifice of Green Capitalism
Bibliographic record
Abstract
The Kunming-Montréal Global Biodiversity Framework (GBF) was widely hailed as a milestone in international efforts to address catastrophic biodiversity decline. However, previous international targets for biodiversity protection have been left largely unmet, raising questions about how to ensure effective implementation of this latest framework. One of the possible implementation tools provided in the GBF is biodiversity offsetting. This involves biodiversity protection or restoration activities that are specifically designed to compensate for (intentional) destruction or degradation of biodiversity elsewhere caused by development. This paper explores the question of whether the use of biodiversity offsets is consistent with the aims of the Global Biodiversity Framework (particularly Target 3, which calls for 30 percent of land and sea areas to be effectively protected by 2030), and, to the extent that it may be inconsistent, what safeguards or alternative approaches could be applied.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".