Project description
Shaping welfare in uncertain data landscapes
In many African countries, welfare systems struggle to target resources due to incomplete data about their populations. National statistical systems are often disjointed, making it difficult for governments to identify who is entitled to welfare benefits. It is difficult to plan and distribute resources in a way that addresses the needs of the most vulnerable. In this context, the ERC-funded ModelFutures project will undertake ethnographic research in Botswana, Ghana, Kenya and Senegal. By exploring how states generate future welfare projections amid these data gaps, the project investigates the role of statistical models in shaping welfare policies. It also examines how experts adapt global models to local contexts, producing new insights into evidence-based welfare planning.
Objective
Welfare systems rely on extensive knowledge about the population to plan, finance, and expend limited resources in a targeted manner. In view of their often-fragmented national identification and statistical systems, Africas emerging welfare states are thus facing significant challenges in their capacity to establish categories of welfare entitlement and to target groups for intervention. Led by the PI, ModelFutures team will carry out ground-breaking, comparative ethnographic research at the statistics-welfare nexus of four African country cases Ghana, Senegal, Kenya and Botswana to understand: How do states generate truths about future welfare in contexts of uncertain knowledge about the population and its wellbeing? The projects threefold objectives aim to (1) trace statistical modelling practices in the context of multi-facetted uncertainties about the population and its environs; (2) analyse the impact of adaptations and creative data practices on quantitative truth claims; and (3) connect statistical future-making and anticipatory welfare politics in sites of statistical innovation. ModelFutures ambition significantly extends beyond the state of the art of our understanding of statistical world-making by developing a novel concept of vernacular prediction that attunes to experts skilful adaptations of globally circulating computational models and standards, while attending to their modelling practices co-constitution with symbolic commitments and situated infrastructural arrangements. Combining expertise from anthropology, science and technology studies, and population statistics, ModelFutures generates novel theory on the production of evidence-based welfare policies in contexts of contested claims to the validity of data, methods, and the anticipatory politics that play out between short-term mitigation efforts and the pursuit of long-term dividends.
Fields of science (EuroSciVoc)
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CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques.
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Programme(s)
- HORIZON.1.1 - European Research Council (ERC) Main Programme
Topic(s)
Funding Scheme
HORIZON-ERC - HORIZON ERC GrantsHost institution
2300 Kobenhavn
Denmark