Corporate disclosures need a biodiversity outcome focus and regulatory backing to deliver global conservation goals
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract To achieve the goals of the Kunming–Montreal Global Biodiversity Framework (KMGBF), agreed by Parties to the Convention on Biological Diversity, there is an urgent need to address the economic drivers of biodiversity loss. The KMGBF includes a target to encourage businesses and financial institutions to disclose their impacts and dependences on biodiversity. While transparent biodiversity disclosures could help shift business operations away from activities that harm biodiversity, the weak target wording implies voluntary and unstandardized disclosures, which tend to be low quality and ineffective. Moreover, examination of scientific and practical insights strongly indicates that the evolving strategy of disclosures led by businesses may prioritize short‐term business and investment interests while neglecting biodiversity outcomes and the wider systemic risks they pose. We argue that there is a risk of limited if not altogether perverse outcomes from the target, where businesses provide ambiguous disclosures that fail to reduce impacts on biodiversity, yet an increase in volume and frequency of disclosures suggests progress toward the target. Consequently, we advocate for a regulatory approach, supported by scientific engagement in the development of disclosure standards and associated policy indicators, to ensure that the emerging response to the KMGBF target on disclosures avoids perverse outcomes and instead results in positive impacts on biodiversity.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it