Mainstreaming revisited: Experiences from eight countries on the role of National Biodiversity Strategies in practice
Bibliographic record
Abstract
Global biodiversity targets have not been met due to weak implementation at the national level. National Biodiversity Strategies and Action Plans (NBSAPs) are central for mainstreaming biodiversity by translating global ambition into national policies. This study analyzes the practical role of global and national biodiversity agendas. Interviews from France, Germany, Honduras, Indonesia, Mexico, Peru, Rwanda, and South Africa show that global targets and NBSAPs have raised awareness, mobilized initiatives, mobilized support for implementation, and fostered accountability. Nevertheless, conflicting interests, weak financial support, and poorly integrated institutional and regulatory structures remain challenges to implementation. Levers for harnessing the role of future NBSAPs to achieve the goals and targets of the Kunming-Montreal Global Biodiversity Framework are: improving communication; defining concrete measures and clear responsibilities; fostering cross-sectoral commitment; enshrining targets into national laws; ensuring adequate public funding; reforming harmful subsidies; ensuring coordination among sectors and levels of governance; and strengthening accountability frameworks.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.043 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.024 | 0.034 |
| Scholarly communication | 0.017 | 0.016 |
| Open science | 0.003 | 0.027 |
| Research integrity | 0.006 | 0.010 |
| 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".