The global aim is to respond to the challenge posed by the H2020 Call ”To develop new perspectives and improved methodologies for capturing the wider societal value of culture, including but also beyond its economic impact”
Scientific papers analyzed
Candidate transition variables extracted
Taxonomy terms defined
Search documents and data for cultural artifacts
Analysis of cultural artifacts using AI.
Transition variables from documents using AI
Social impact indicators from transition variables
Structured resources defining MESOC topics
Impacts of art to health.
Search the documents on cultural artifacts
Search documents and data for cultural artifacts
Search documents based on social impact
Search documents based on cultural domain
Search by cultural domain and social impact
Semantic search on articles. Semantic search describes a search engine’s attempt to generate the most accurate search engine results possible by understanding based on searcher intent, query context, and the relationship between words.Semantic search denotes search with meaning, as distinguished from lexical search where the search engine looks for literal matches of the query words or variants of them, without understanding the overall meaning of the query. Some authors regard semantic search as a set of techniques for retrieving knowledge from richly structured data sources
Analysis of documents with Artificial Intelligence
The system reviews documents to assess the social impact of culture. The review includes analysis of all and individual documents, clustering, summarizing, keyword analysis, and other analytical tools and methods.
Text clustering is the task of grouping a set of texts in such a way that texts in the same cluster are more similar to each other than to those in other clusters. Text clustering algorithms process text and determine if natural clusters (groups) exist in the data.
As computers work with numbers, text has to be transformed into multidimensional numbers as vectors. We use here 4096 dimensional space.The idea is that documents can be represented numerically as vectors of features. The similarity in text can be compared by measuring the distance between these feature vectors. Objects that are near each other should belong to the same cluster. Objects that are far from each other should belong to different clusters
Topic model is a type of statistical model for discovering the abstract "topics" that occur in a collection of documents. Topic modeling is a frequently used text-mining tool for discovery of hidden semantic structures in a text body.
contextual elements, which can be measured ensuring that the cultural policy or practice under inspection is generating public value and/or affecting, at least to some extent, the target individuals or groups.
Transition variables show us the paths of transformation and the channels of materialization of the impact, in a richer and more complex analysis than the cause-effect linearity.
Transition processes are complex and non linear, but induce changes across time.
Transition variables enable a better contextualisation of the concrete processes in concrete places and periods.They can be observed in shorter periods of time than the expected impacts.
We can obtain them from the experiences recorded in scientific literature and in grey material reports (evaluation reports, memos, programs...).
We are using a pre-trained BERT (Bidirectional Encoder Representations from Transformers) model for a text classification task. The main goal of the model in a text classification task is to categorize a text into one of the predefined labels or tags, and in our case to read sentences and to classify them as normal sentences or potential transition variables.
BERT is pretrained on unlabeled data extracted from BooksCorpus, which has 800M words, and from Wikipedia, which has 2,500M words. We are fine-tuning it, by training on our sample of 3500 sentences each of which is manually marked, as trans. variable or anything else.
Search and view of transition variables based on social impact
Search and view of transition variables based on cultural domain
Search and view of transition variables based on cultural domain and social impact
Semantic search on Transition variables
Transformative process - Using AI to define social impacts
‘Social impacts’ is the term which describes the changes in the quality of life of the local residents. Changes that affect individuals’ surroundings (architecture, arts, customs, rituals etc.) constitute cultural impacts.
The enormous range of impacts include arts and crafts through to the fundamental behaviour and beliefs of individuals and collective groups (Sharpley, 2008; Sharpley & Telfer, 2014).
We are using DialogGPT to define impacts. DialogGPT is a pre-trained, state-of-the-art language model developed by OpenAI that generates human-like text responses in a conversational context. It uses the latest advancements in artificial intelligence and deep learning to generate context-aware and coherent responses based on the input text. DialogGPT is trained on a massive dataset of text, allowing it to have a diverse range of knowledge and understanding of different topics. This technology has the potential to revolutionize the way we interact with machines and can be utilized in various industries, including customer service, education, and entertainment.
List of impacts of culture impacts
List of MESOC defined impacts
Policy measures to increase impacts
Structured resources that can be used to improve access to information for related to three crossover themes of the new European Agenda for Culture: 1) Health and Wellbeing, 2) Urban and Territorial Renovation and 3) People’s Engagement and Participation. The Mesoc taxonomy is not simple vocabulary, it is rather unique knowledge base. Through rich metadata and links, the MESOC taxonomy provide powerful tools for knowledge creation, complex research, and structural model of the Societal Dimension of Culture.
Structered resources for MESOC taxonomy
Graph depicting all structured resources
Tree view of MESOC taxonomy
Graph depicting taxonomy and MESOC links.
Research by MESOC Serapeum has shown that the use of art in health care and in communities can have a variety of benefits for health outcomes..
Impact of arts to health - Results from transition variables
Impact of arts to health - Results from MESOC Taxonomy
Impact of arts to health - Results from form ChatGPT .
Policy measures increasing impact of Arts to Health form ChatGPT .