Operationalizing transformative change for business in the context of Nature Positive
Bibliographic record
Abstract
The Kunming-Montreal Global Biodiversity Framework (GBF) includes a specific target for reducing businesses' negative impacts on biodiversity and increasing their positive impacts to contribute toward the GBF mission and vision. "Nature Positive" is also emerging as a rallying call for mainstreaming the GBF. Merely tinkering with business as usual will not deliver these ambitions; transformative change is needed. However, how to operationalize transformative change toward Nature Positive and the GBF through meaningful actions and targets remains unclear, risking confusion, greenwashing, and failure to achieve global goals. This perspective draws on literature on social change to offer a practical framework for understanding and operationalizing transformative change for business toward a Nature Positive future. We define and describe the role of transformative change within a Nature Positive ambition and summarize the different types and scales of actions companies could take. This framework could help to plan mutually reinforcing actions and improve accountability for Nature Positive claims.
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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 | 0.023 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.008 | 0.054 |
| Scholarly communication | 0.019 | 0.018 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".