{"id":3416,"date":"2020-09-29T19:08:50","date_gmt":"2020-09-29T17:08:50","guid":{"rendered":"https:\/\/www.proamazonia.org\/?p=3416"},"modified":"2020-12-11T13:42:32","modified_gmt":"2020-12-11T18:42:32","slug":"inflexiones-positivas-los-valores-atipicos-importan","status":"publish","type":"post","link":"https:\/\/www.proamazonia.org\/en\/inflexiones-positivas-los-valores-atipicos-importan\/","title":{"rendered":"POSITIVE DEVIANCES: OUTLIERS MATTER"},"content":{"rendered":"

In early 2020, the UNDP Sustainable Development Goals Accelerator Labs Network, together with the GIZ Data Lab (Deutsche Gesselschaft f\u00fcr Internationale Zusammenarbeit) with University of Manchester technical support, launched a study to explore the potential of Data Powered Positive Deviance (DPPD) for sustainable development.<\/span><\/p>\r\n

UNDP Accelerator Labs and GIZ projects in Ecuador, Mexico, Niger and Somalia\/Somaliland are using administrative records, satellite imagery, and urban, social media and mobility data to identify positive deviances in various contexts, such as the advance of the agricultural frontier in the Amazon forest, public spaces in Mexico City and pastoralist villages in Somaliland. Our goal is to identify well-developed, adapted local solutions that can help communities overcome the development challenges they face.<\/span><\/p>\r\n\r\n\r\n\r\n

A \u201cPositive\u201d case study<\/b><\/p>\r\n\r\n\r\n\r\n

The Positive Deviance approach assumes that, in each community, there are individuals or groups whose unique behaviors or resilience mechanisms allow them, unlike their peers, to find better solutions to problems with the same resources. The goal of Positive Deviance interventions is to identify attributes or traits of individuals and groups in order to understand what differentiates them from others. These traits will serve as a reference for other members of the community so that they can address common problems with accessible resources. In short, it does not seek negative experiences in order to correct them; rather, positive deviances from the status quo are identified to try to replicate them collectively in situ. Perhaps the best-known experience with Positive Deviance was a ‘Save the Children’ initiative in northern Vietnam in the 1990s. At the time, 65% of children between the ages of 1 and 4 in Vietnamese villages were malnourished. \u2018Save the Children\u2019 had a clear purpose: to find a sustainable and large-scale solution, based on local resources, that would show results in six months.<\/span><\/p>\r\n

NGO staff in the territory set out to find families where children did not show signs of malnutrition. The team identified several of these \u201cpositive deviances\u201d and discovered that caregivers added shrimp, crab and fish to the household diet, even though these foods were considered inappropriate for young children. With this idea, Save the Children developed activities with local communities to rehabilitate hundreds of malnourished children, and teach families how to feed their children at home, on their own<\/span>.<\/span><\/p>\r\n\r\n\r\n\r\n

\r\n
\"\"\r\n
Family from Vietnam with a member of Save the Children talking about malnutrition \u00a9 Positive Deviance Collaborators<\/span><\/figcaption>\r\n<\/figure>\r\n<\/div>\r\n\r\n\r\n\r\n

Since the 1990s, Positive Deviance approaches have been applied to other developmental problems, such as school year losses, neonatal mortality, pregnancy termination, violence against women, health services, HIV transmission, and antibiotic-resistant bacteria infections in hospitals.<\/span><\/p>\r\n\r\n\r\n\r\n

Big Data, Positive Deviance, and Development.<\/b><\/p>\r\n\r\n\r\n\r\n

Traditional positive deviance initiatives rely heavily on primary and secondary data sources, e.g. data collected directly from the study target group, and reused data (such as censuses), respectively. Unfortunately, these data are very expensive (primary sources) and concentrated in aggregate variables or in variables that are irrelevant for positive atypical behaviors (secondary sources). They can also be limited in time (a momentary photo rather than information that evolves over time).<\/span><\/p>\r\n

Recent work by GIZ Labs, the UN Global Pulse Lab Jakarta, and the University of Manchester, show that it is possible to utilize big data, for example, high quantity, speed and variety of data generated mainly by individuals, as we carry out daily activities, to discover positive deviances in agricultural communities, and isolate factors that differentiate them from their peers.<\/span><\/p>\r\n

Inspired by this, the UNDP Accelerator Labs and GIZ Data Lab with the technical support of the University of Manchester, launched a series of pilot programs in early 2020 in the Data Powered Positive Deviance – DPPD initiative, which called for proposals from interested laboratories<\/span>.<\/span><\/p>\r\n\r\n\r\n\r\n

\r\n
\"\"\r\n
\r\n

The Data Powered Positive Deviance Initiative has several pilot programs in a few countries to understand how big data can contribute to development challenges \u00a9 GIZ Data Lab<\/span><\/p>\r\n

 <\/p>\r\n

17 Accelerator Labs and 2 GIZ projects responded with 23 proposals.<\/b> Four were selected from Ecuador, Mexico, Niger and Somaliland, and are based on an aligned work between Accelerator labs and GIZ portfolios at the local level, and their potential to use of big data to identify positive deviances in the challenges of deforestation, violence against women, sustainable agriculture and water management.<\/span><\/p>\r\n

This is what we are working on:<\/span><\/p>\r\n

Ecuador:<\/b> Mitigating Deforestation Through Sustainable Livestock<\/span><\/p>\r\n<\/figcaption>\r\n<\/figure>\r\n<\/div>\r\n\r\n\r\n\r\n