A standard lexicon of terms for area‐based conservation version 1.0
Bibliographic record
Abstract
Target 3 in the Kunming-Montreal Global Biodiversity Framework (GBF) calls for protecting at least 30% of the world's lands and waters in area-based conservation approaches by 2030. This ambitious 30×30 target has spurred great interest among policy makers, practitioners, and researchers in defining and measuring the effectiveness of these types of approaches. But along with this broad interest, there has also been a proliferation of terms and their accompanying abbreviations used to describe different types of conservation areas and their governance, planning, management, and monitoring. The lack of standard terms is hindering the use and assessment of area-based approaches to conserve the world's biodiversity. It is difficult to track progress toward GBF Target 3 or to share learning with other practitioners if different groups of people are using different words to describe the same concept or similar words to talk about different concepts. To address this problem, the International Union for Conservation of Nature's World Commission on Protected Areas commissioned a task force to review existing terms and recommend a standard English-language lexicon for this field based on key criteria. The results were definitions of 37 terms across 6 categories, including types of protected and additional conservation areas (e.g., protected area, additional conservation area), sets of these areas (protected area network, protected area system), their governance and management (governance, rightsholders), assessment (effectiveness, equitability), spatial planning (key biodiversity area), and action planning (value, outcome, objective). Our standard lexicon can provide a common language for people who want to use it and a shared reference point that can be used to translate various terms used by different groups. The common understanding provided by the lexicon can serve as a foundation for collaborative efforts to improve the policies, implementation, assessments, research, and learning about this important set of conservation approaches.
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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 | 0.006 | 0.018 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.013 | 0.019 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.034 | 0.028 |
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".