Mapping human-nature archetypes to guide global biodiversity, food security, and land-use policy
Bibliographic record
Abstract
Reconciling biodiversity conservation, food security, and sustainable agriculture at global scale requires a clear understanding of regional social-ecological opportunities and challenges. This understanding helps untap regional contributions to better achieve global policy targets, such as those framed in the Kunming-Montreal Global Biodiversity Framework (GBF). Yet previous global syntheses of social-ecological interlinkages remain limited in thematic and spatial detail, restricting the discussion of regional contributions and targeted policy implementation. Here, we present 25 human-nature archetypes derived from clustering of global social-ecological data revealing regional opportunities and challenges for meeting global policy targets. Our results differentiate regions with large conservation opportunities from those well suited for ecological restoration or ecological intensification. They highlight the widespread need for improving governance to enhance food security and re-design agricultural systems. Overall, our analysis supports international and national decision makers in tailoring GBF targets to regional specificities in order to more effectively achieve global sustainability goals.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".