Biodiversity and human well-being: an essential link for sustainable development
Bibliographic record
Abstract
As society strives to transition towards more sustainable development pathways, it is important to properly conceptualize the link between biodiversity (i.e. genes, traits, species and other dimensions) and human well-being (HWB; i.e. health, wealth, security and other dimensions). Here, we explore how published conceptual frameworks consider the extent to which the biodiversity-HWB links are being integrated into public discourse and scientific research and the implications of our findings for sustainable development. We find that our understanding has gradually evolved from seeing the value of biodiversity as an external commodity that may influence HWB to biodiversity as fundamental to HWB. Analysis of the literature trends indicates increasing engagement with the terms biodiversity, HWB and sustainable development in the public, science and policy spheres, but largely as independent rather than linked terms. We suggest that a consensus framework for sustainable development should include biodiversity explicitly as a suite of internal variables that both influence and are influenced by HWB. Doing so will enhance clarity and help shape coherent research and policy priorities. We further suggest that the absence of this link in development can inadvertently lead to a ratcheting down of biodiversity by otherwise well-meaning policies. Such biotic impoverishment could lock HWB at minimum levels or lead to its decline and halt or reverse progress in achieving sustainable development.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".