Priority Questions and Horizon Scanning for Conservation: A Comparative Study
Bibliographic record
Abstract
Several projects aimed at identifying priority issues for conservation with high relevance to policy have recently been completed in several countries. Two major types of projects have been undertaken, aimed at identifying (i) policy-relevant questions most imperative to conservation and (ii) horizon scanning topics, defined as emerging issues that are expected to have substantial implications for biodiversity conservation and policy in the future. Here, we provide the first overview of the outcomes of biodiversity and conservation-oriented projects recently completed around the world using this framework. We also include the results of the first questions and horizon scanning project completed for a Mediterranean country. Overall, the outcomes of the different projects undertaken (at the global scale, in the UK, US, Canada, Switzerland and in Israel) were strongly correlated in terms of the proportion of questions and/or horizon scanning topics selected when comparing different topic areas. However, some major differences were found across regions. There was large variation among regions in the percentage of proactive (i.e. action and response oriented) versus descriptive (non-response oriented) priority questions and in the emphasis given to socio-political issues. Substantial differences were also found when comparing outcomes of priority questions versus horizon scanning projects undertaken for the same region. For example, issues related to climate change, human demography and marine ecosystems received higher priority as horizon scanning topics, while ecosystem services were more emphasized as current priority questions. We suggest that future initiatives aimed at identifying priority conservation questions and horizon scanning topics should allow simultaneous identification of both current and future priority issues, as presented here for the first time. We propose that further emphasis on social-political issues should be explicitly integrated into future related projects.
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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 | 0.025 | 0.053 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".