817 research outputs found

    'From Each according to Ability; To Each according to Needs': Origin, Meaning, and Development of Socialist Slogans

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    There are three slogans in the history of Socialism that are very close in wording, viz. the famous Cabet-Blanc-Marx slogan: "From each according to his ability; To each according to his needs"; the earlier Saint-Simon-Pecqueur slogan: "To each according to his ability; To each according to his works"; and the later slogan in Stalin’s Soviet Constitution: "From each according to his ability; To each according to his work." We will consider the following questions regarding these slogans: a) What are the earliest occurrences of each of these slogans? b) Where does the inspiration for each half of each slogan come from? c) What do the Saint-Simonians mean by “To each according to his ability”? d) What do they mean by “To each according to his works”? e) What motivates the shift from “To each according to his ability” to “From each according to his ability”? f) How should we envisage the progression toward “To each according to his needs”? g) What is the distinction between from “To each according to his works” and “To each according to his work”

    Non-Euclidean geometry and political economy How Jacques Rueff explained unemployment in England (1919-1931)

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    This paper provides new perspectives on the French liberal economist Jacques Rueff (1896-1978), especially as regards his early writings on unemployment. We aim to show that Rueff distinguishes the root causes of permanent unemployment in England (1919-1931) based upon an interesting reading of non-Euclidean geometry. Controversially, this enables him to locate the cause of unemployment in the stickiness of the wage/price ratio. Hence, arguing that reality remains inaccessible in itself, Rueff focuses on a succession of variables (price, wage, unemployment), supplemented by his concepts of rational ego and the reasoning machine, in order to approach this reality

    On commercial gluts Unexpected affinities between Jean-Baptiste Say and the Saint-Simonians

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    A standard reading in the history of economic thought considers Saint-Simonianism to be embodied in the works of a set of European social thinkers including Robert Owen, William Godwin and Sismondi, all of whom are seen as standing in strict opposition to the doctrine of laissez-faire. This article, however, argues that, during the first quarter of the 19th century, the Saint-Simonians and the liberal economist Jean-Baptiste Say can be seen to adopt convergent views on commercial gluts. First, it shows how the Saint-Simonians and Say both see undersupply and lack of industry as causes of gluts. Next, we assert that their intellectual affinities are also visible in their belief that increasing production remains an appropriate solution for gluts. Finally, this convergence is explained by their common heritage: Saint-Simonianism is embedded in a neo-Smithian tradition for which Say can be taken as representative. We argue that this legacy explains their convergence

    Gap-filling eddy covariance methane fluxes : Comparison of machine learning model predictions and uncertainties at FLUXNET-CH4 wetlands

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    Time series of wetland methane fluxes measured by eddy covariance require gap-filling to estimate daily, seasonal, and annual emissions. Gap-filling methane fluxes is challenging because of high variability and complex responses to multiple drivers. To date, there is no widely established gap-filling standard for wetland methane fluxes, with regards both to the best model algorithms and predictors. This study synthesizes results of different gap-filling methods systematically applied at 17 wetland sites spanning boreal to tropical regions and including all major wetland classes and two rice paddies. Procedures are proposed for: 1) creating realistic artificial gap scenarios, 2) training and evaluating gap-filling models without overstating performance, and 3) predicting halfhourly methane fluxes and annual emissions with realistic uncertainty estimates. Performance is compared between a conventional method (marginal distribution sampling) and four machine learning algorithms. The conventional method achieved similar median performance as the machine learning models but was worse than the best machine learning models and relatively insensitive to predictor choices. Of the machine learning models, decision tree algorithms performed the best in cross-validation experiments, even with a baseline predictor set, and artificial neural networks showed comparable performance when using all predictors. Soil temperature was frequently the most important predictor whilst water table depth was important at sites with substantial water table fluctuations, highlighting the value of data on wetland soil conditions. Raw gap-filling uncertainties from the machine learning models were underestimated and we propose a method to calibrate uncertainties to observations. The python code for model development, evaluation, and uncertainty estimation is publicly available. This study outlines a modular and robust machine learning workflow and makes recommendations for, and evaluates an improved baseline of, methane gap-filling models that can be implemented in multi-site syntheses or standardized products from regional and global flux networks (e.g., FLUXNET).Peer reviewe

    Pseudorapidity and transverse momentum dependence of flow harmonics in pPb and PbPb collisions

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    Differential cross section measurements for the production of a W boson in association with jets in proton–proton collisions at √s = 7 TeV

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    Measurements are reported of differential cross sections for the production of a W boson, which decays into a muon and a neutrino, in association with jets, as a function of several variables, including the transverse momenta (pT) and pseudorapidities of the four leading jets, the scalar sum of jet transverse momenta (HT), and the difference in azimuthal angle between the directions of each jet and the muon. The data sample of pp collisions at a centre-of-mass energy of 7 TeV was collected with the CMS detector at the LHC and corresponds to an integrated luminosity of 5.0 fb[superscript −1]. The measured cross sections are compared to predictions from Monte Carlo generators, MadGraph + pythia and sherpa, and to next-to-leading-order calculations from BlackHat + sherpa. The differential cross sections are found to be in agreement with the predictions, apart from the pT distributions of the leading jets at high pT values, the distributions of the HT at high-HT and low jet multiplicity, and the distribution of the difference in azimuthal angle between the leading jet and the muon at low values.United States. Dept. of EnergyNational Science Foundation (U.S.)Alfred P. Sloan Foundatio

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