A semi-automated approach to policy-relevant evidence synthesis: Combining natural language processing, causal mapping, and graph analytics for public policy
- DOI
- 10.21203/rs.3.rs-3285731/v1
- Published
- 2023-08-23
- Container
- Not recorded
- Publisher
- Springer Science and Business Media LLC
- Open access
- unknown
Credibility signals
uncertain Score 60/100 under policy 1.0.0. This is a metadata assessment, not a judgment of the paper's conclusions.
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Cite this work
BibTeX
@article{allodium:10.21203/rs.3.rs-3285731/v1,
title = {A semi-automated approach to policy-relevant evidence synthesis: Combining natural language processing, causal mapping, and graph analytics for public policy},
author = {Rory Hooper and Nihit Goyal and Kornelis Blok and Lisa Scholten},
year = {2023},
doi = {10.21203/rs.3.rs-3285731/v1},
url = {https://doi.org/10.21203/rs.3.rs-3285731/v1}
}RIS
TY - JOUR TI - A semi-automated approach to policy-relevant evidence synthesis: Combining natural language processing, causal mapping, and graph analytics for public policy AU - Rory Hooper AU - Nihit Goyal AU - Kornelis Blok AU - Lisa Scholten PY - 2023 DO - 10.21203/rs.3.rs-3285731/v1 UR - https://doi.org/10.21203/rs.3.rs-3285731/v1 ER -
APA
Hooper, R., Goyal, N., Blok, K., & Scholten, L. (2023). A semi-automated approach to policy-relevant evidence synthesis: Combining natural language processing, causal mapping, and graph analytics for public policy. https://doi.org/10.21203/rs.3.rs-3285731/v1
Source records
- crossref · retrieved 2026-09-25T22:06:56.995Z