An Urgent Call to Integrate the Health Sector into the Post-2020 Global Biodiversity Framework
Bibliographic record
Abstract
There is a rapidly closing window of opportunity to stop biodiversity loss and secure the resilience of all life on Earth. In December 2022, Parties to the United Nations (UN) Convention on Biological Diversity (CBD) will meet in Montreal, Canada, to finalize the language and terms of the Post-2020 Global Biodiversity Framework (Post-2020 GBF). The Post-2020 GBF aims to address the shortcomings of the previous Strategic Plan on Biodiversity 2011–2020, by introducing a Theory of Change, that states that biodiversity protection will only be successful if unprecedented, transformative changes are implemented effectively by Parties to the CBD. In this policy perspective, we explore the implications of the Theory of Change chosen to underpin the Post-2020 GBF, specifically that broad social transformation is an outcome that requires actors to be specified. We detail how the health sector is uniquely positioned to be an effective actor and ally in support of the implementation of the Post-2020 GBF. Specifically, we highlight how the core competencies and financial and human resources available in the health sector (including unique knowledge, skill sets, experiences, and established trust) provide a compelling, yet mostly untapped opportunity to help create and sustain the enabling conditions necessary to achieve the goals and targets of the framework. While by no means a panacea for the world’s biodiversity problems, we posit that explicitly omitting the health sector from the Post-2020 GBF substantially weakens the global, collective effort to catalyze the transformative changes required to safeguard biodiversity.
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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 | 0.061 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.029 |
| Scholarly communication | 0.024 | 0.025 |
| Open science | 0.005 | 0.027 |
| Research integrity | 0.047 | 0.045 |
| Insufficient payload (model declined to judge) | 0.022 | 0.004 |
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".