Building a stakeholder-led common vision increases the expected cost-effectiveness of biodiversity conservation
Bibliographic record
Abstract
Uniting diverse stakeholders through communication, education or building a collaborative 'common vision' for biodiversity management is a recommended approach for enabling effective conservation in regions with multiple uses. However, socially focused strategies such as building a collaborative vision can require sharing scarce resources (time and financial resources) with the on-ground management actions needed to achieve conservation outcomes. Here we adapt current prioritisation tools to predict the likely return on the financial investment of building a stakeholder-led vision along with a portfolio of on-ground management strategies. Our approach brings together and analyses expert knowledge to estimate the cost-effectiveness of a common vision strategy and on-ground management strategies, before any investments in these strategies are made. We test our approach in an intensively-used Australian biodiversity hotspot with 179 threatened or at-risk species. Experts predicted that an effective stakeholder vision for the region would have a relatively low cost and would significantly increase the feasibility of on-ground management strategies. As a result, our analysis indicates that a common vision is likely to be a cost-effective investment, increasing the expected persistence of threatened species in the region by 9 to 52%, depending upon the strategies implemented. Our approach can provide the maximum budget that is worth investing in building a common vision or another socially focused strategy for building support for on-ground conservation actions. The approach can assist with decisions about whether and how to allocate scarce resources amongst social and ecological actions for biodiversity conservation in other regions worldwide.
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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 | 0.015 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".