Introducing Elinor for monitoring the governance and management of area‐based conservation
Bibliographic record
Abstract
Monitoring the governance and management effectiveness of area-based conservation has long been recognized as an important foundation for achieving national and global biodiversity goals and enabling adaptive management. However, there are still many barriers that prevent conservation actors, including those affected by governance and management systems from implementing conservation activities and programs and from gathering and using data on governance and management to inform decision-making across spatial scales and through time. We explored current and past efforts to assess governance and management effectiveness and barriers actors face in using the resulting data and insights to inform conservation decision-making. To help overcome these barriers, we developed Elinor, a free and open-source monitoring tool that builds on the work of Nobel Prize winner Elinor Ostrom to facilitate the gathering, storing, sharing, analyzing, and use of data on environmental governance and management across spatial scales and for areas under different governance and management types. We consider the process of codesigning and piloting Elinor with conservation scientists and practitioners and the main components of the assessment and online data system. We also consider how Elinor complements existing approaches by addressing governance and management in a single assessment at a high level for different types of area-based conservation, providing flexible options for data collection, and integrating a data system with an assessment that can support data use and sharing across different spatial scales, including global monitoring of the Global Biodiversity Framework. Although challenges will continue, the process of developing Elinor and the tool itself offer tangible solutions to barriers that prevent the systematic collection and use of governance and management data. With broader uptake, Elinor can play a valuable role in enabling more effective, inclusive, and durable area-based conservation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".