School for Advanced Studies in the Social Sciences

About: School for Advanced Studies in the Social Sciences is a facility organization based out in Villejuif, France. It is known for research contribution in the topics: Politics & Context (language use). The organization has 1230 authors who have published 2084 publications receiving 57740 citations. The organization is also known as: Ecole des Hautes Etudes en Sciences Sociales & EHESS.

How did uncommon disorders become ‘rare diseases’? History of a boundary object

Abstract: The category of ‘rare diseases’ has been in growing use in the fields of public health and patient advocacy for the past 15 years in Europe. In this socio-historical inquiry, I argue that this category, which appeared initially as a by-product of the orphan drug issue in the United States of America is a boundary object. As such, it has different specific local uses: a meaningless category for physicians, it relates to the patients’ experience of illness, whereas the pharmaceutical industry first considered it as being synonymous with small markets and then with innovation. Public bodies contributed to framing a common and blurred use, based on a statistical definition whose purpose was to foster co-operation between the four groups involved in the issue. In the definition process of the category of rare diseases, the key actors were the patients and public bodies, not medical professionals or the pharmaceutical industry.

Future expansion of agriculture and pasture acts to amplify atmospheric CO2 levels in response to fossil fuel and land-use change emissions

Abstract: The expansion of crop and pastures to the detriment of forests results into an increase in atmospheric CO2. A first obvious cause is the loss of forest biomass and soil carbon during and after conversion. A second, generally ignored cause, is the reduction of the residence time of carbon when for example forests or grasslands are converted to cultivated land. This decreases the sink capacity of the global terrestrial biosphere, and thereby may amplify the atmospheric CO2 rise due to fossil and land-use carbon release. For the IPCC-A2 future scenario, characterized by high fossil and high land-use emissions, we show that the land-use amplifier effect adds 61 ppm extra CO2 in the atmosphere by 2100 as compared to former treatment of land-use processes in carbon models. Investigating the individual contribution of each of the 6 land-use transitions (forest - crop, forest - pasture, grassland -crop) to the amplifier effect indicates that the clearing of forest and grasslands to arable lands explains most of the CO2 amplification. The amplification effect is 50% higher than in a previous analysis by the same authors which did not consider neither the deforestation to pastures nor the ploughing of grasslands. Such an amplification effect is further examined in sensitivity tests where the net primary productivity is considered independant of atmospheric CO2. We also show that land-use changes which have already occurred in the recent past have a strong inertia at releasing CO2, and will contribute to about 1/3 of the amplification effect by 2100. These results suggest that there is an additionnal atmospheric benefit of preserving pristine ecosystems with high turnover times.

A thorough evaluation of the Language Environment Analysis (LENA) system

Abstract: In the previous decade, dozens of studies involving thousands of children across several research disciplines have made use of a combined daylong audio-recorder and automated algorithmic analysis called the LENAⓇ system, which aims to assess children's language environment. While the system's prevalence in the language acquisition domain is steadily growing, there are only scattered validation efforts on only some of its key characteristics. Here, we assess the LENAⓇ system's accuracy across all of its key measures: speaker classification, Child Vocalization Counts (CVC), Conversational Turn Counts (CTC), and Adult Word Counts (AWC). Our assessment is based on manual annotation of clips that have been randomly or periodically sampled out of daylong recordings, collected from (a) populations similar to the system's original training data (North American English-learning children aged 3-36 months), (b) children learning another dialect of English (UK), and (c) slightly older children growing up in a different linguistic and socio-cultural setting (Tsimane' learners in rural Bolivia). We find reasonably high accuracy in some measures (AWC, CVC), with more problematic levels of performance in others (CTC, precision of male adults and other children). Statistical analyses do not support the view that performance is worse for children who are dissimilar from the LENAⓇ original training set. Whether LENAⓇ results are accurate enough for a given research, educational, or clinical application depends largely on the specifics at hand. We therefore conclude with a set of recommendations to help researchers make this determination for their goals.