| Personal Page | http://clementinecttn.github.io/ |
| ORCID | https://orcid.org/0000-0002-2452-3901 |
| ERC SEGUE | https://www.erc-segue.nl/ |
| Reading blog | https://unequalread.hypotheses.org/ |
A 4-year PhD Position in Agent-Based Modelling of Urban Economic Segregation with me and the ERC project SEGUE at TU Delft, to apply modular social simulation, empirical calibration & spatially explicit population synthesis to a socially relevant topic.
In this working paper, we propose a set of guidelines and toolbox to design and automate several steps of systematic literature reviews so that urbanists with limited experience in SLR can focus on the tasks of reading and synthetizing the knowledge collected!
Lit reviews are key to academic research. Systematic literature reviews (SLR) help with summarising the evidence on a topic in an organised, transparent and reproducible way, but they can be long/difficult to implement, in particular in interdisciplinary fields such as urbanism
🌍 May 22-23, 2024🌎
I will teach a two-day online workshop about modern approaches to work on and
visualize spatial data in R.
Learn more at https://buff.ly/3UKQ9kF
Email me if you have any questions!
What happens to #analytical #urban #models when they circulate across geographical and disciplinary spaces? What are the consequences of urban models' #mobility on our understanding of cities?
Our Paper in Urban Studies is now out.
This manuscript presents a comprehensive review of the use of Artificial Intelligence (AI) in Systematic Literature Reviews (SLRs). A SLR is a rigorous and organised methodology that assesses and integrates previous research on a given topic. Numerous tools have been developed to assist and partially automate the SLR process. The increasing role of AI in this field shows great potential in providing more effective support for researchers, moving towards the semi-automatic creation of literature reviews. Our study focuses on how AI techniques are applied in the semi-automation of SLRs, specifically in the screening and extraction phases. We examine 21 leading SLR tools using a framework that combines 23 traditional features with 11 AI features. We also analyse 11 recent tools that leverage large language models for searching the literature and assisting academic writing. Finally, the paper discusses current trends in the field, outlines key research challenges, and suggests directions for future research.
*we = Kyri Janssen, Jerome Francisco Conceicao, Claudiu Forgaci, Ana Petrović, Daniele Cannatella, Clémentine Cottineau, Javier San Millàn Tejedor, Manuel Garcia Alvarez & Selin Kubilay, with invaluable support from Paula Martinez Lavanchy
> Huge thank you to all involved!
Jakub Nowosad