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Systematic Review of Outcomes Contracts - Collaboration (SyROCCo)

Welcome to our Systematic Review of Outcomes Contracts Collaboration (SyROCCo) Machine Learning tool. This tool allows users to navigate and explore data extracted from nearly 2000 academic and grey literature publications related to outcomes-based contracting. It is based on the broadest and deepest evidence review undertaken in the field of outcomes contracting to date. SyROCCo is the product of a collaboration between the Government Outcomes Lab and machine learning experts from the University of Warwick.

The tool is currently a beta version and we would welcome any feedback.

How to cite this work: Fang, Z., Arana-Catania, M., van Lier, F.-A., Outes Velarde, J., Bregazzi, H., Airoldi, M., Carter, E. & Procter, R. (2024). SyROCCo: enhancing systematic reviews using machine learning. Data & Policy, 6, e39. DOI: 10.1017/dap.2024.33

The dataset behind SyROCCo can be found here.

Want to provide feedback? More about SyROCCo

Articles

Article Relevance Year Link

Results summary

  • Distribution per policy sector

    • Geographical entities

      • Legal entities

        • Organisational entities

          • Financial mechanisms

            • Sustainable development goals

              • Indigo projects

                Terms of use

                The Systematic Review of Outcomes Contracts Collaboration (SyROCCo) Machine Learning Tool is licensed under the Creative Commons Attribution 4.0 International license. This license allows reusers to distribute, remix, adapt, and build upon the material in any medium or format for noncommercial purposes only, and only so long as attribution is given to the creators.

                Your privacy is important to the Government Outcomes Lab. Our privacy policy can be found here.



                Suggested citation

                Fang, Z., Arana-Catania, M., van Lier, F.-A., Outes Velarde, J., Bregazzi, H., Airoldi, M., Carter, E. & Procter, R. (2024). SyROCCo: enhancing systematic reviews using machine learning. Data & Policy, 6, e39. DOI: 10.1017/dap.2024.33

                • Author

                • PaperID (Covidence #)

                • Published year

                • DOI

                • Policy sector

                • Abstract

                • Geographical entities

                  • Legal entities

                  • Organisational entities

                    • Financial mechanisms entities

                      • Sustainable development goals

                        • Mentioned countries

                          • Mentioned Indigo projects

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                              Terms of use

                              The Systematic Review of Outcomes Contracts Collaboration (SyROCCo) Machine Learning Tool is licensed under the Creative Commons Attribution 4.0 International license. This license allows reusers to distribute, remix, adapt, and build upon the material in any medium or format for noncommercial purposes only, and only so long as attribution is given to the creators.

                              Your privacy is important to the Government Outcomes Lab. Our privacy policy can be found here.



                              Suggested citation

                              Fang, Z., Arana-Catania, M., van Lier, F.-A., Outes Velarde, J., Bregazzi, H., Airoldi, M., Carter, E. & Procter, R. (2024). SyROCCo: enhancing systematic reviews using machine learning. Data & Policy, 6, e39. DOI: 10.1017/dap.2024.33