Better evidence, better decisions, better environment: emergent themes from the first environmental evidence conference
Bibliographic record
Abstract
The first international Collaboration for Environmental Evidence (CEE) conference took place in August 2016 at the Swedish Museum of Natural History in Stockholm with nearly 100 participants from 14 countries. This conference reflected and contributed to the growth of a global network of people interested in the production and use of evidence syntheses in environmental management. The conference also provided an opportunity to identify emerging themes and reflect on those ideas and perspectives to help direct future activities of the CEE and the broader community. An increasingly engaged community of practice was evident but there is uneven distribution of experience, resources, capacity, and commitment to evidence synthesis in different sectors and regions. There is much opportunity to bring academics, practitioners, and other partners together which will help to further demonstrate impact of evidence synthesis activities and enhance relevance. As the discipline evolves there is growing interest in rapid evidence synthesis but the benefits and risks of that approach remain unclear. There was also a recognition that improvements in empirical science will enhance the likelihood that more studies can be fully exploited as part of evidence synthesis. There are opportunities for capacity building, engaging the next generation (e.g., students), and enhancing connections within and beyond the CEE community to advance evidence-based environmental management. It is our desire that this paper will serve as a template for future CEE activities (i.e., where to invest resources) but also as an invitation to those that were unable to attend to participate in CEE and the evidence-based environmental management movement in whichever ways resonate with them.
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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 | 0.230 | 0.231 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.027 | 0.035 |
| Scholarly communication | 0.057 | 0.034 |
| Open science | 0.008 | 0.048 |
| Research integrity | 0.020 | 0.055 |
| Insufficient payload (model declined to judge) | 0.004 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".