Societal Rather than Governmental Change: Religious Discrimination in Muslim-Majority Countries after the Arab Uprisings

This study examines shifts in governmental religion policy and societal discrimination against religious minorities in Muslim-Majority states after the Arab Uprisings by using the Religion and State round 3 (RAS3) dataset for the years 2009-2014 and by focusing on 49 Muslim-majority countries and territories. We build on threads of literature on religious pluralism in transitional societies to explain the changes in governmental religion policy and societal discrimination against religious minorities after the Arab Uprisings. This literature predicts a rise in all forms of discrimination in Arab Uprising states as compared to other Muslim-majority states, and an even more significant rise in societal religious discrimination since societal behavior can change more quickly than government policy, especially at times of transition. The results partially conform to these predictions. There was no significant difference in the shifts in governmental religion policy between Arab Uprising and other Muslim-Majority states, but societal religious discrimination increased substantially in Arab Uprising states as compared to non-Arab Uprising states. Understanding the nature of religion policies and religious discrimination provides further opportunities to unveil the dynamics of regional politics as well as conflict prevention in the region

Detecting execution failures using learned action models

Planners reason with abstracted models of the behaviours they use to construct plans. When plans are turned into the instructions that drive an executive, the real behaviours interacting with the unpredictable uncertainties of the environment can lead to failure. One of the challenges for intelligent autonomy is to recognise when the actual execution of a behaviour has diverged so far from the expected behaviour that it can be considered to be a failure. In this paper we present an approach by which a trace of the execution of a behaviour is monitored by tracking its most likely explanation through a learned model of how the behaviour is normally executed. In this way, possible failures are identified as deviations from common patterns of the execution of the behaviour. We perform an experiment in which we inject errors into the behaviour of a robot performing a particular task, and explore how well a learned model of the task can detect where these errors occur

Rethinking \u27What Counts\u27 As Accountability

The current accountability impasse suggests it may be time to rethink core concepts, as well as the field’s underlying theories of change. The idea of accountability is malleable, ambiguous — and contested. This fuzziness poses challenges for both theory and practice – how do we know what strategies bolster accountability – or whether accountability produces its expected effects? This think piece recognizes the challenge of defining ‘what counts’ as accountability, unpacks a longstanding theory of change - that sunshine is the best disinfectant - and considers some information-based reform initiatives to identify missing links in the causal chain between transparency and accountability

Patterns of discrimination, grievances and political activity among Europe's Roma: a cross-sectional analysis

Der vorliegende Beitrag untersucht die Muster von Diskriminierung, Missständen und politischen Aktivitäten unter europäischen Roma unter Verwendung von Daten des 'Minorities at Risk' Projektes. Das Modell, das hier geprüft wird, ist ein zweistufiges Modell, das davon ausgeht, dass Diskriminierung zu Missständen führt und diese wiederum zu Protest und Rebellion. Die Resultate zeigen, dass die Roma, im Allgemeinen, diesem Modell entsprechen, sich aber in einigen wichtigen Besonderheiten unterschieden. Die Roma sind historisch gesehen - und auch wenn man die Gegenwart betrachtet - eine der am meisten diskriminierten Minderheiten in Ost- und Westeuropa. Diese Diskriminierung wurde von Menschenrechtsreports und Wissenschaftlern dokumentiert, es wurden jedoch keine großen Querschnittsstudien zu diesem Thema durchgeführt. Der Autor liefert daher die vorliegende empirische Analyse der Muster der Diskriminierung. Die Ergebnisse werden dann mit den Ergebnissen allgemeiner Untersuchungen ethnischer Konflikte aus dem 'Minorities at Risk' Projekt verglichen. (ICD

Taking scale into account in transparency and accountability initiatives

We know that gaining access to information and raising citizen voices are not the same as achieving accountability. It is important to look beyond the symptoms of accountability failure, and consider how to tackle the causes. This short research summary discusses different understandings of scale, one important aspect of making transparency and accountability initiatives more strategic. Scale shapes both the causes of accountability failure and the tactics and strategies needed to address it.DFIDUSAIDSIDAOmidyar Networ

The Political Construction of Accountability Keywords

Terminology in the accountability field is ambiguous, encompassing both top-down, technocratic control initiatives and bottom‑up efforts to challenge the abuse of power and promote equity. The main proposition is that communicating accountability strategies should rely on conceptual and cross-cultural translation rather than awkward attempts at direct linguistic translation. To illustrate how accountability keywords are both politically constructed and contested, this article briefly reflects on the origins, circulation, and transformation of six relevant terms: transparency, the right to know, whistle-blower, advocacy, openwashing, and social accountability – including reflections from accountability advocates from Pakistan, Guatemala, and the Philippines. The conclusion calls for a two-track approach to communicate public accountability strategies, which involves (1) searching within popular cultures to find existing terms or phrases that can be repurposed, and (2) inventing new discourses that communicate ideas about public accountability that resonate with culturally grounded common-sense understandings.Open Society Foundations, Vozes Desiguais/Unequal Voices, Future Health Systems consortium, the Impact Initiative and Health Systems Globa

Real-Time, Variable-Depth Tillage for Managing Soil Compaction in Cotton Production

Cotton is one of the most important crops in the southern USA with an estimated production value of $6 billion. Cotton root growth is often hindered in the Southeastern U.S. due to the presence of root-restricting soil layers. Soils in this region have three distinct layers, the A horizon, the E horizon and the Bt horizon. The E horizon is often plagued with a hardpan layer that has a much higher bulk density than optimum for crop production. This limits the ability of the plant roots to penetrate into the Bt horizon for uptake of water and nutrients, therefore, reducing yields, limiting productivity, and making plants more susceptible to drought stress. Tillage must be used to temporarily remove this compacted soil layer to allow root growth to depths needed to sustain plants during periods of drought. However, due to significant variability in depth and thickness of hardpan layers in Coastal Plain soils, applying uniform-depth tillage over the entire field may be either too shallow to fracture the hardpan or deeper than required resulting in excess fuel consumption and inefficient use of energy. Therefore, significant savings in tillage energy could be achieved by adjusting tillage depth to match soil’s physical properties. However, there is currently no equipment commercially available to automatically control the tillage depth to match the soil physical properties. Therefore, the objective of this project was to develop and test equipment for controlling tillage depth “on-the-go” to match soil physical parameters, and plant responses in cotton production. The “Clemson Intelligent Plow” was developed by modifying an existing four-row subsoiler into a variable depth tillage platform, which could change the tillage depth from zero to 45 cm (18 in) on-the-go. Site-specific tillage operations reduced fuel consumption by 45% compared to conventional constant-depth tillage. Only 20% of the test field required tillage at recommended depth for Coastal Plain regions (15 inches deep). Cotton taproots in the variable-depth tillage plots were 96% longer than those in the no-till plots (15.4 vs. 7.8 inches). Statistically, there were no differences in cotton lint yield between conventional and the variable-depth tillage. Deep tillage (conventional or variable-rate) increased cotton lint yields by 20% compared to no-till