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| http://dx.doi.org/10.1128/JCM.00728-19 | DOI Listing |
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Young people's experiences of informal kinship care in Luwero, Uganda.
Child Youth Serv Rev
April 2024
London School of Hygiene & Tropical Medicine (LSHTM), United Kingdom.
There is strong evidence that outside parental care, informal kinship care is the most practiced, sustainable and affordable form of childcare in SSA (sub-Saharan Africa). As a longstanding cultural tradition, informal kinship care embraces childcare as the responsibility of all extended family members, and often the wider community. However, over the past decades, informal kinship care has become gradually strained by political, economic and social conditions, such as: legacies of colonialism, increasing levels of poverty and inequality, instability, or infectious diseases.
View Article and Find Full Text PDFOver twenty years of publications in Ecology: Over-contribution of women reveals a new dimension of gender bias.
PLoS One
September 2024
Institute of Geographical Sciences, Freie Universität Berlin, Berlin, Germany.
Biographical features like social and economic status, ethnicity, sexuality, care roles, and gender unfairly disadvantage individuals within academia. Authorship patterns should reflect the social dimension behind the publishing process and co-authorship dynamics. To detect potential gender biases in the authorship of papers and examine the extent of women's contribution in terms of the substantial volume of scientific production in Ecology, we surveyed papers from the top-ranked journal Ecology from 1999 to 2021.
View Article and Find Full Text PDFAugmenting the availability of historical GDP per capita estimates through machine learning.
Proc Natl Acad Sci U S A
September 2024
Center for Collective Learning, Artificial and Natural Intelligence Toulouse Institute, Institut de Recherche en Informatique de Toulouse, Université de Toulouse, 31000 Toulouse, France.
Can we use data on the biographies of historical figures to estimate the GDP per capita of countries and regions? Here, we introduce a machine learning method to estimate the GDP per capita of dozens of countries and hundreds of regions in Europe and North America for the past seven centuries starting from data on the places of birth, death, and occupations of hundreds of thousands of historical figures. We build an elastic net regression model to perform feature selection and generate out-of-sample estimates that explain 90% of the variance in known historical income levels. We use this model to generate GDP per capita estimates for countries, regions, and time periods for which these data are not available and externally validate our estimates by comparing them with four proxies of economic output: urbanization rates in the past 500 y, body height in the 18 century, well-being in 1850, and church building activity in the 14 and 15 century.
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