Operationalizing transformative change for business in the context of nature positive
Bibliographic record
Abstract
The Kunming-Montreal Global Biodiversity Framework (GBF) sets a specific target for reducing the private sector's negative impacts on biodiversity and increasing positive impacts, as part of overall efforts to halt and reverse biodiversity loss within the coming decade.In parallel, 'nature positive' is emerging as an inclusive and ambitious rallying call that aligns with the GBF.Yet tinkering with business as usual will not deliver these ambitions; calls for transformative change in business's relationship with biodiversity are increasingly strong.However there remains a lack of clarity on how to operationalize transformative change in the context of nature positive, particularly how to develop meaningful actions and measurable targets.This gap risks confusion, greenwashing, and failure to achieve global goals.This article aims to fill this gap, by drawing on existing literature on social change to offer a practical framework for understanding and operationalizing transformative change for business and biodiversity.We define and describe the role of transformative change towards a nature positive ambition and summarize the different types and scales of transformative actions that companies could take into a simple framework, which we illustrate with case studies from food retail and mining.This framework could be used to help companies develop and plan transformative actions, set targets, and monitor progress over time, as well as hold them accountable to 'transformative' claims; however, it can only contribute to a nature positive commitment if it is implemented in parallel with meaningful actions to avoid, reduce, restore, and compensate for contemporary attributable impacts.We invite companies to test our framework for their own planning, decision-making and disclosures, to advance meaningful application of transformative actions towards delivery of transformative change for a nature positive future.
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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 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.009 | 0.054 |
| Scholarly communication | 0.020 | 0.019 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 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".