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Institution

London School of Economics and Political Science

EducationLondon, United Kingdom
About: London School of Economics and Political Science is a education organization based out in London, United Kingdom. It is known for research contribution in the topics: Politics & Population. The organization has 8759 authors who have published 35017 publications receiving 1436302 citations.


Papers
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BookDOI
01 Jan 2010
TL;DR: This summary makes the case for performance measurement as key tool for policy-makers endeavouring to improve health systems in the European Region, demonstrating that if governments invest in health they can expect those resources to be used well.
Abstract: This summary makes the case for performance measurement as key tool for policy-makers endeavouring to improve health systems in the European Region. It highlights the various elements required of a comprehensive health system performance measurement framework; pinpoints how performance measurement can be used in practice; and stresses the role of government stewardship in securing improved performance. It reviews existing evidence and provides examples of the empirical application of performance measures, demonstrating that if governments invest in health they can expect those resources to be used well.

497 citations

Journal ArticleDOI
TL;DR: It is argued that for many common machine learning problems, although in general the authors do not know the true (objective) prior for the problem, they do have some idea of a set of possible priors to which the true prior belongs.
Abstract: A Bayesian model of learning to learn by sampling from multiple tasks is presented. The multiple tasks are themselves generated by sampling from a distribution over an environment of related tasks. Such an environment is shown to be naturally modelled within a Bayesian context by the concept of an objective prior distribution. It is argued that for many common machine learning problems, although in general we do not know the true (objective) prior for the problem, we do have some idea of a set of possible priors to which the true prior belongs. It is shown that under these circumstances a learner can use Bayesian inference to learn the true prior by learning sufficiently many tasks from the environment. In addition, bounds are given on the amount of information required to learn a task when it is simultaneously learnt with several other tasks. The bounds show that if the learner has little knowledge of the true prior, but the dimensionality of the true prior is small, then sampling multiple tasks is highly advantageous. The theory is applied to the problem of learning a common feature set or equivalently a low-dimensional-representation (LDR) for an environment of related tasks.

496 citations

Book
01 Jan 2004
TL;DR: Barry Buzan as mentioned in this paper proposes a new theoretical framework that can be used to address globalisation as a complex political interplay among state and non-state actors, and highlights the idea of primary institutions as the central contribution of English school theory.
Abstract: In this 2004 book, Barry Buzan offers an extensive critique and reappraisal of the English school approach to International Relations. Starting on the neglected concept of world society and bringing together the international society tradition and the Wendtian mode of constructivism, Buzan offers a new theoretical framework that can be used to address globalisation as a complex political interplay among state and non-state actors. This approach forces English school theory to confront neglected questions about both its basic concepts and assumptions, and about the constitution of society in terms of what values are shared, how and why they are shared, and by whom. Buzan highlights the idea of primary institutions as the central contribution of English school theory and shows how this both differentiates English school theory from realism and neoliberal institutionalism, and how it can be used to generate distinctive comparative and historical accounts of international society.

495 citations

Journal ArticleDOI
24 Dec 2018
TL;DR: This paper conducted preregistered replications of 28 classic and contemporary published findings, with protocols that were peer reviewed in advance, to examine variation in effect magnitudes across samples and settings, and found that very little heterogeneity was attributable to the order in which the tasks were performed or whether the task were administered in lab versus online.
Abstract: We conducted preregistered replications of 28 classic and contemporary published findings, with protocols that were peer reviewed in advance, to examine variation in effect magnitudes across samples and settings. Each protocol was administered to approximately half of 125 samples that comprised 15,305 participants from 36 countries and territories. Using the conventional criterion of statistical significance (p < .05), we found that 15 (54%) of the replications provided evidence of a statistically significant effect in the same direction as the original finding. With a strict significance criterion (p < .0001), 14 (50%) of the replications still provided such evidence, a reflection of the extremely high-powered design. Seven (25%) of the replications yielded effect sizes larger than the original ones, and 21 (75%) yielded effect sizes smaller than the original ones. The median comparable Cohen’s ds were 0.60 for the original findings and 0.15 for the replications. The effect sizes were small (< 0.20) in 16 of the replications (57%), and 9 effects (32%) were in the direction opposite the direction of the original effect. Across settings, the Q statistic indicated significant heterogeneity in 11 (39%) of the replication effects, and most of those were among the findings with the largest overall effect sizes; only 1 effect that was near zero in the aggregate showed significant heterogeneity according to this measure. Only 1 effect had a tau value greater than .20, an indication of moderate heterogeneity. Eight others had tau values near or slightly above .10, an indication of slight heterogeneity. Moderation tests indicated that very little heterogeneity was attributable to the order in which the tasks were performed or whether the tasks were administered in lab versus online. Exploratory comparisons revealed little heterogeneity between Western, educated, industrialized, rich, and democratic (WEIRD) cultures and less WEIRD cultures (i.e., cultures with relatively high and low WEIRDness scores, respectively). Cumulatively, variability in the observed effect sizes was attributable more to the effect being studied than to the sample or setting in which it was studied.

495 citations

Journal ArticleDOI
TL;DR: The authors provide an overview of the current state of the literature on the relationship between social media; political polarization; and political "disinformation", a term used to encompass a wide range of types of information about politics found online.
Abstract: The following report is intended to provide an overview of the current state of the literature on the relationship between social media; political polarization; and political “disinformation,” a term used to encompass a wide range of types of information about politics found online, including “fake news,” rumors, deliberately factually incorrect information, inadvertently factually incorrect information, politically slanted information, and “hyperpartisan” news. The review of the literature is provided in six separate sections, each of which can be read individually but that cumulatively are intended to provide an overview of what is known — and unknown — about the relationship between social media, political polarization, and disinformation. The report concludes by identifying key gaps in our understanding of these phenomena and the data that are needed to address them.

494 citations


Authors

Showing all 9081 results

NameH-indexPapersCitations
Ichiro Kawachi149121690282
Amartya Sen149689141907
Peter Hall132164085019
Philippe Aghion12250773438
Robert West112106153904
Keith Beven11051461705
Andrew Pickles10943655981
Zvi Griliches10926071954
Martin Knapp106106748518
Stephen J. Wood10570039797
Jianqing Fan10448858039
Timothy Besley10336845988
Richard B. Freeman10086046932
Sonia Livingstone9951032667
John Van Reenen9844040128
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
2023135
2022457
20212,030
20201,835
20191,636
20181,561