The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Organising evidence for environmental management decisions: a ‘4S’ hierarchy
Open accessAbstract
Making decisions informed by the best-available science is an objective for many organisations managing the environment or natural resources. Yet, available science is still not widely used in environmental policy and practice. We describe a '4S' hierarchy for organising relevant science to inform decisions. This hierarchy has already revolutionised clinical practice. It is beginning to emerge for environmental management, although all four levels need substantial development before environmental decision-makers can reliably and efficiently find the evidence they need. We expose common bypass routes that currently lead to poor or biased representation of scientific knowledge. We argue that the least developed level of the hierarchy is that closest to decision-makers, placing synthesised scientific knowledge into environmental decision support systems.
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Cites
Jakku, E., & Thorburn, P. J. (2010) · Agricultural Systems · doi:10.1016/j.agsy.2010.08.007 · Cited by 204
Roberts, P. D., Stewart, G. B., & Pullin, A. S. (2006) · Biological Conservation · doi:10.1016/j.biocon.2006.04.034 · Cited by 137
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Metadata
- Authors
- Dicks, L. V., Walsh, J. C., & Sutherland, W. J.
- Venue
- Trends in Ecology & Evolution
- Publisher
- Elsevier BV
- Published
- 2014-11
- Volume
- 29
- Issue
- 11
- Pages
- 607-613
- ISSN
- 0169-5347 (ISSN-L)
- OpenAlex
- W2049710689
- Cited by
- 249
- References
- 2
- FWCI
- 13.00
- Type
- journal article
- Language
- en
- Open access
- hybrid
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- Dicks, L. V., Walsh, J. C., & Sutherland, W. J. (2014). Organising evidence for environmental management decisions: a ‘4S’ hierarchy. Trends in Ecology & Evolution. https://doi.org/10.1016/j.tree.2014.09.004
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
