Paper details
| Paper authors | François ENTEN |
| In panel on | Trust in Humanitarian Numbers? Bringing Critical Data Studies into Humanitarian Studies |
| Paper presenter(s) will be presenting |
In-Person / |
Abstract
"More than 30 million people are on the verge of starvation": this is how the Secretary General of the United Nations recently warned of the risks of "imminent famine" in several countries. These alarming statements by international agencies are based on the IPC (Integrated Food Security Phase Classification) system for collecting and processing data on food insecurity and malnutrition. Derived from pre-existing models of early warning systems (EWS), the IPC is presented as a vehicle for consensus among humanitarian actors to gauge the severity of the situation in inaccessible areas or when data is of poor quality or non-existent. The results obtained are formulated in intermediate categories of "probable famine" thus allowing for early warning. On the other hand, this intermediate classification, quoted "conditionally" by the experts, becomes an assertion when the media or the communication services of humanitarian organizations reappropriate it by associating it with images of emaciated children, with a generalization of disproportionate numbers and often with references to historical famines.
In a context of ultra-mediatization, how do the members of the IPC combine the multiplicity of knowledge and sources, the methodological imprecision and lack of data, with the will to maintain an alert function? How are the approximate data of the expert register relayed by the communication relays of humanitarian organizations, the media and social networks? And what effects do they have on decision-making and the (on-)activation of emergency actions? Based on interviews conducted with professionals, the paper will describe two case studies, those of the Yemen crisis (2019) and the recent crisis of the Malagasy Kéré (2020-21), one being over-mediatized while the other is a "silent crisis", both of which are inaccessible data fields.
Presenters
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