Objective
The study of academic achievement as function of ‘gender’ has received an increasing interest. Nevertheless, this topic seems still topical, at least for the following reasons: until now, some explanatory variables have been picked out but, probably, further unexplored factors exist; 2) the relationship between those factors has not been disclosed yet, i.e. it is not clear if interaction effects between them exist. In particular, the sectorial literature has investigated gender-related stereotypes’ effects on academic achievement but it has not studied possible interactions between stereotypes and the other variables frequently used to study students’ performance; 3) the inter-national comparison is undoubtedly an adequate method to deepen the study of gender-based gap. Nevertheless, the big international research institutes use national data, and it is clear that, when we use big data (such as national ones), almost automatically, some dangerous compensations occur, leading data to a false medium value. Our approach based also on the intra-national comparison is able to avoid those compensations. In fact, the comparisons between macro-geographical clusters produce better data, i.e. data that reproduce the reality more realistically, with three main consequences: 1) to improve the probability of picking out factors that can explain academic achievement depend on gender variable; 2) to guide local (inter-/national) policies; 3) to produce new data that can be used in future researches. Moreover, also the cross-national comparison (between macro-geographical areas within different Countries) could produce new important data because, through it, some possible “regularities” across different Countries can be disclosed, such as, for example, the same interaction effect between stereotypes and social-economic variables, etc. It could be an interesting headway in the field because it could indicate results that are true independently from specific context.
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.
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Programme(s)
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
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H2020-EU.1.3. - EXCELLENT SCIENCE - Marie Skłodowska-Curie Actions
MAIN PROGRAMME
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H2020-EU.1.3.2. - Nurturing excellence by means of cross-border and cross-sector mobility
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Topic(s)
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.
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.
Funding Scheme
Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
MSCA-IF-EF-ST - Standard EF
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Call for proposal
Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
(opens in new window) H2020-MSCA-IF-2016
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Net EU financial contribution. The sum of money that the participant receives, deducted by the EU contribution to its linked third party. It considers the distribution of the EU financial contribution between direct beneficiaries of the project and other types of participants, like third-party participants.
M13 9PL Manchester
United Kingdom
The total costs incurred by this organisation to participate in the project, including direct and indirect costs. This amount is a subset of the overall project budget.