Managing environmental knowledge networks to navigate complexity
Bibliographic record
Abstract
Environmental knowledge networks (EKNs) link research collaborators in a common purpose to produce data and knowledge to better understand social-ecological phenomena and address environmental challenges. Over recent years, as scientists have grappled with how to produce data and actionable knowledge for conservation and sustainability, more EKNs have been established. Although each network is founded for its own purposes and maintains its own goals and ways of operating, these networks are generally managed by scientists to produce knowledge to advance science and decision making. In this Insight article, we articulate key qualities and benefits of EKNs and shows how EKNs can address grand challenges that cannot be answered by a single team or institution, create a diverse, vibrant culture of science and community of practice, and provide innovative solutions and knowledge to society. We also discuss challenges of EKN governance, and how challenges may vary with a network’s development. Finally, based on a synthesis of structured discussions about key issues in EKN management, we share recommendations and best practices, emphasizing management practices that are inclusive, reflexive, adaptive, and flexible, so that others may benefit from our experience leading EKNs.
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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 | 0.002 | 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) | 0.001 | 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 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".