The Nature Positive Journey for Business: A research agenda to enable private sector contributions to the global biodiversity framework.
Bibliographic record
Abstract
The 2022 Kunming-Montreal Global Biodiversity Framework calls upon the private sector to take substantial action to mitigate its negative impacts on biodiversity and contribute towards nature recovery.The term 'Nature Positive' has gained traction in biodiversity conservation discourse to describe both a societal goal and the ambitions of individual organisations to halt and reverse nature loss.However, enabling businesses to contribute towards Nature Positive outcomes will require major shifts in the way businesses and society operate, and research that can help guide and prioritise business actions.As a group of researchers and consultants working at the interface between business and biodiversity, we propose a conceptual model through which private sector contributions to a Nature Positive future could be realised and use it to identify priority research questions.The key questions address: i) sectoral strategic options, ii) methods and approaches individual businesses can implement to inform these strategies, iii) systemic driving forces that influence private sector action, and iv) how outcomes are measured to deliver Nature Positive contributions.Collaborations between researchers, businesses and industry bodies are needed to co-design and implement research, where there is currently no coordinated approach to identify and fund priority research areas for Nature Positive themes.A clearly structured and prioritised research agenda is vital to guide effective, equitable and timely action by businesses.
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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 | 0.027 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.009 |
| Scholarly communication | 0.019 | 0.023 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.009 | 0.006 |
| Insufficient payload (model declined to judge) | 0.015 | 0.003 |
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".