Project description
Reviewing memories to tackle educational inequality
Stereotypes fuel biased behaviours, amplifying socioeconomic disparities, particularly within educational settings. These preconceived notions, held by both teachers and students, hinder equitable opportunities for diverse groups. With this in mind, the ERC-funded SOFIA project will use cutting-edge datasets, quasi-natural experiments and randomised trials to dive deep into stereotype formation and behaviour biases. Specifically, SOFIA will study the role of selective memory in gender stereotypes, immigrant exposure effects, and self-stereotyping in Chile, Finland, and Italy. By blending behavioural insights with solid evidence, the project’s findings will inform policy debates on dismantling discrimination and promoting educational equity for all. It holds promise in reshaping societal narratives and fostering inclusive learning environments.
Objective
                                Stereotypes are often at the origin of biased behaviour and can contribute to the widening of socioeconomic inequalities in diverse societies. This is especially true in the schooling context, where both teachers and students may hold negative stereotypes towards certain groups. The overarching goal of this proposal is to study the formation of stereotypes and test policies designed to mitigate educational inequalities, building on insights from behavioural economics and causal machine learning techniques.
The proposed research combines several innovative aspects: (i) cutting edge datasets merging administrative data with newly collected surveys, including psychological measures and incentivized experiments (ii) quasi-natural experiments to shed light on the determinants of stereotypes and biased behaviour, and (iii) randomized controlled trials to test scalable and cost-effective policies.
SOFIA is composed of three workpackages (WP), focusing respectively on evidence from Italy, Finland, and Chile. WP1 provides innovative evidence on the role of selective memory in the formation of gender stereotypes for adolescents and teachers (Project A), and evidence on how causal machine learning techniques can be used to mitigate inequalities (Project B). WP2 focuses on the implications of exposure to immigrants on the development of stereotypes and inter-ethnic relationships (Project C) and on how to improve social cohesion through innovative interventions that exploit behavioural insights (Project D). WP3 investigates the role of self-stereotypes in explaining limited access to opportunities in education (Project E).
The proposal speaks to the policy debate on how to effectively mitigate discrimination to foster educational achievements of disadvantaged or underrepresented groups. It is my hope that the combination of innovative solutions inspired by behavioural insights and solid evidence generated through credible empirical strategies will help inform this debate
                            
                                Fields of science (EuroSciVoc)
                                                                                                            
                                            
                                            
                                                CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See:   The European Science Vocabulary.
                                                
                                            
                                        
                                                                                                
                            CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: The European Science Vocabulary.
- social sciences educational sciences didactics
- social sciences sociology social issues social inequalities
- social sciences sociology demography human migrations
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                                        Project’s keywords as indicated by the project coordinator. Not to be confused with the EuroSciVoc taxonomy (Fields of science)
                                        
                                    
                                
                            
                            
                        Project’s keywords as indicated by the project coordinator. Not to be confused with the EuroSciVoc taxonomy (Fields of science)
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                  HORIZON.1.1 - European Research Council (ERC)
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                  Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.
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(opens in new window) ERC-2023-STG
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20136 Milano
Italy
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