This challenge arose directly from conversations prior to the opening of the competition, between a group of Iscte teachers (under the aegis of the IA>AP Competence Center, https://iaap.iscte-iul.pt) and members of the PlanApp board. Among the tasks that were considered interesting for exploring and testing possibilities, the following stood out (in the area of AI / Data Science): 1. Analysis of topics from the outputs of the “Building Bridges” workshops 2. Extraction, processing and analysis of inequality monitoring indicators 3. Extraction of fields in public policy planning documents 4. Analysis of demographic indicators Task 1 aims to test the possibilities of semi-automatic processing of conference output in order to reduce processing time and/or flag up particularly relevant parts for the PlanApp of the documents resulting from the “Science and Public Policy Workshops: How to build bridges?”. In these workshops, participants are invited to write short texts with proposals, difficulties and challenges related to the interaction between academia and public policy, but the manual processing of this amount of text is demanding in terms of reading time for the organizers, and a semi-automatic processing is beneficial, which can not only highlight the most addressed topics, but also point out the most relevant parts (or those that address more original topics). The aim of Task 2 is to help with the implementation of the Inequalities Report, where PlanAPP has developed quantitative (description and simulation) and qualitative analyses of various dimensions of inequality. One of the main difficulties in this process is the continuous need to merge information from different sources. The aim of this task is to map the various sources and organize the process of merging and cleaning data, which will allow for a more stable analysis of these indicators. Task 3 is a task of structuring semi-structured documents. Public policy planning documents are usually semi-structured texts in chapters/sections, and reading them thoroughly to highlight the most relevant parts is one of the most time-consuming processes for the PlanApp team. What the research team sets out to do is assess the possibilities of automatically tagging documents, with special attention to setting targets and deadlines from planning documents. Task 4 aims to contribute to the merging, processing and visualization of demographic indicator data (selected by the PlanApp). The aim of this task is to simplify the simultaneous processing and cross-referencing of a wide range of indicators used in planning.

No records found.

No records found.

No records found.

No records found.

No records found.