Suchergebnisse

After President Xi Jinping came to office, the Chinese government tightened its ideological control over establishment intellectuals and particularly the university teachers. This project aims to answer the question of why and when an authoritarian state chooses to do so. It focuses on the CCP's policy toward establishment intellectuals under Xi and explores the most applicable explanation for this policy shift. Based on the existing literature on the cyclical model of state-intellectual relations in China, we propose a new model of "Dual Methods in State-Intellectual Relations." The model demonstrates that the CCP has employed dual methods of repression and co-optation on intellectuals adopting different roles. Leaders choose repression when they perceive that the legitimacy of their rule is challenged externally or internally by criticism and collective actions led by intellectuals. In this case, the goal of repression is to either set up a new "red line" or warn intellectuals that they have crossed it.

Yang reviews Critical Mission: Essays on Democracy Promotion by Thomas Carothers.

Machine learning algorithms pervade contemporary society. They are integral to social institutions, inform processes of governance, and animate the mundane technologies of daily life. Consistently, the outcomes of machine learning reflect, reproduce, and amplify structural inequalities. The field of fair machine learning has emerged in response, developing mathematical techniques that increase fairness based on anti-classification, classification parity, and calibration standards. In practice, these computational correctives invariably fall short, operating from an algorithmic idealism that does not, and cannot, address systemic, Intersectional stratifications. Taking present fair machine learning methods as our point of departure, we suggest instead the notion and practice of algorithmic reparation. Rooted in theories of Intersectionality, reparative algorithms name, unmask, and undo allocative and representational harms as they materialize in sociotechnical form. We propose algorithmic reparation as a foundation for building, evaluating, adjusting, and when necessary, omitting and eradicating machine learning systems.

Abstract. As the risk of a forest fire is largely influenced by weather, evaluating its tendency under a changing climate becomes important for management and decision making. Currently, biases in climate models make it difficult to realistically estimate the future climate and consequent impact on fire risk. A distribution-based scaling (DBS) approach was developed as a post-processing tool that intends to correct systematic biases in climate modelling outputs. In this study, we used two projections, one driven by historical reanalysis (ERA40) and one from a global climate model (ECHAM5) for future projection, both having been dynamically downscaled by a regional climate model (RCA3). The effects of the post-processing tool on relative humidity and wind speed were studied in addition to the primary variables precipitation and temperature. Finally, the Canadian Fire Weather Index system was used to evaluate the influence of changing meteorological conditions on the moisture content in fuel layers and the fire-spread risk. The forest fire risk results using DBS are proven to better reflect risk using observations than that using raw climate outputs. For future periods, southern Sweden is likely to have a higher fire risk than today, whereas northern Sweden will have a lower risk of forest fire.

Context. Giant pandas (Ailuropoda melanoleuca) are restricted to six mountain ranges at the edge of the Tibetan Plateau. One of these ranges, the Qinling Mountains, contains the highest density of giant pandas and is home to ~20% of those remaining in the wild. Commercial logging and other developments have resulted in habitat fragmentation, and an efficient and powerful conservation network is now needed for the species in this area. Aims. This study sought to assess giant panda habitat and estimate the carrying capacity of this reserve network. Our goal was to improve the function and carrying capacity of the reserve network and facilitate population growth and gene flow among subpopulations of giant pandas. Methods. We use habitat suitability models to assess the efficacy of conservation networks. With estimation of carrying capacity by home range, we can reveal issues facing reserves and populations of endangered species they contain. Here, we define key habitat, linkages, corridors and overall connectivity and then use habitat network modelling and spatial analyses to design a conservation landscape for giant pandas across their Qinling Mountains stronghold. Key results. We found that 91% of giant panda sightings were in suitable or marginally suitable habitat. The total area of giant panda habitat present in the Qinling Mountains is ~1600 km2 fragmented across four key habitat blocks by national roads or other human activity. The current nature reserve network encompasses 71% of available suitable habitat and 62% of available marginal habitat, meaning a significant proportion of panda habitat remains outside the current conservation network. We found that giant panda reserves across this region are not equal in their carrying capacity; some reserves contain an overabundance of giant pandas and the wellbeing of these populations are in doubt. Conclusions. Our results highlight the potential risk of high densities and bamboo flowering events to the safety of giant pandas. With poor population size and heavy isolation, small populations will not persist without translocation. Implication. Redrawing the reserve network to correct localised problems may improve the function of the giant panda protection system, build capacity in the reserve network, and decrease human–wildlife conflict. We propose a new reserve and adjustment of the borders and region for three reserves.