Justice, power and informal settlements: Understanding the juridical view of property rights in Central Asia

The article examines how judges and lawyers struggle to legitimise and normalise private property rights against attempts by poor and migrant groups to politicise housing and social needs in Central Asia. It will discuss the juridical understanding of justice and equality in relation to property rights violations on the outskirts of major cities in Kyrgyzstan and Kazakhstan. It will argue that the juridical system is central in construing property rights and obligations, and in so doing social inequalities are legitimised and naturalised in a neoliberalising post-Soviet space. The article uses the concepts of 'the moral economy' and 'the juridical field' to examine how judges and lawyers justify and normalise their ways of interpreting and ordering the social world

Data for: Reinterpreting the Enemy: Geopolitical Beliefs and the Attribution of Blame in the Nagorno-Karabakh Conflict

The CSV file is the raw data from the experiment. The Word files include discussion groups, by assigned narrative

Data for: Reinterpreting the Enemy: Geopolitical Beliefs and the Attribution of Blame in the Nagorno-Karabakh Conflict

The CSV file is the raw data from the experiment. The Word files include discussion groups, by assigned narrative.THIS DATASET IS ARCHIVED AT DANS/EASY, BUT NOT ACCESSIBLE HERE. TO VIEW A LIST OF FILES AND ACCESS THE FILES IN THIS DATASET CLICK ON THE DOI-LINK ABOV

Evaluating the healthiness of chain-restaurant menu items using crowdsourcing: a new method

ObjectiveTo develop a technology-based method for evaluating the nutritional quality of chain-restaurant menus to increase the efficiency and lower the cost of large-scale data analysis of food items.DesignUsing a Modified Nutrient Profiling Index (MNPI), we assessed chain-restaurant items from the MenuStat database with a process involving three steps: (i) testing 'extreme' scores; (ii) crowdsourcing to analyse fruit, nut and vegetable (FNV) amounts; and (iii) analysis of the ambiguous items by a registered dietitian.ResultsIn applying the approach to assess 22 422 foods, only 3566 could not be scored automatically based on MenuStat data and required further evaluation to determine healthiness. Items for which there was low agreement between trusted crowd workers, or where the FNV amount was estimated to be >40 %, were sent to a registered dietitian. Crowdsourcing was able to evaluate 3199, leaving only 367 to be reviewed by the registered dietitian. Overall, 7 % of items were categorized as healthy. The healthiest category was soups (26 % healthy), while desserts were the least healthy (2 % healthy).ConclusionsAn algorithm incorporating crowdsourcing and a dietitian can quickly and efficiently analyse restaurant menus, allowing public health researchers to analyse the healthiness of menu items