La imaginación en el sistema de Ramón Llull

Abstract not availabl

Policy, politics and pendidikan: teacher deployment in Indonesia

The problem of uneven teacher deployment has long been recognized in Indonesia. With an overall ratio of approximately one teacher to 16 primary school students (1:13 in juniorsecondary), there is a substantial oversupply of teachers. However, these are poorly distributed. Urban schools are commonly overstaffed while schools in rural and isolated areas are understaffed. This situation creates inefficiencies within the system and penalizes poor and marginalized communities. The end result is a disparity in education quality between schools, and an overall constraint to quality improvement. A joint Five Minister Edict was issued in 2011, requiring all districts to redistribute teachers evenly and according to need. The edict was issued by the Ministers of Education, Religious Affairs, Finance, Home Affairs and State Bureaucracy. Although this regulation required the redistribution to be implemented by end of 2013, few districts have complied. The major challenges to implementation are vested political interests and local resistance. Teachers and their spouses (many of whom are civil servants) commonly provide a political support base for local politicians and are rewarded with attractive placements. In addition, districts lack the capacity to accurately map teacher distribution or conduct analysis to identify policy solutions. As teachers are under the authority of districts, the central and provincial governments have played no significant role. The USAID-funded PRIORITAS project developed and implemented a teacher deployment program known as Penataan dan Pemerataan Guru (PPG) in 23 districts. Using the national education database (DAPODIK) and working with local partners from the districts, universities and province-level education quality assurance agencies (LPMP), the project has successfully mapped teacher distribution, developed policy solutions, conducted public consultations (multistakeholder forums) and is supporting implementation in these districts. The program is being expanded to more districts and a national policy dialogue is underway. The analysis of teacher needs is based on minimum service standards and requirements of the curriculum (2006 and 2013). Results from the initial sample of 23 districts were collated at national level and are summarized in this paper. The policy solutions vary depending on local contexts. These include teacher transfers, incentives for remote placements, school mergers, multi-grade teaching, mobile teachers, and retraining teachers to enable them to teach different subjects or levels. Such policies have the potential to greatly improve the quality of education throughout the country by ensuring that schools are properly staffed, improving equity, and improving system efficiency, releasing funds for quality improvement

Accidental intralenticular dexamethasone intravitreal implant with the resolution of macular oedema in central retinal vein occlusion

No abstract availabl

Resistant And Susceptible BIB Designs

Statistic

Moderate deviations of minimum contrast estimators under contamination

Since statistical models are simplifications of reality, it is important in estimation theory to study the behavior of estimators also under distributions (slightly) different from the proposed model. In testing theory, when dealing with test statistics where nuisance parameters are estimated, knowledge of the behavior of the estimators of the nuisance parameters is needed under alternatives to evaluate the power. In this paper the moderate deviation behavior of minimum contrast estimators is investigated not only under the supposed model, but also under distributions close to the model. A particular example is the (multivariate) maximum likelihood estimator determined within the proposed model. The set-up is quite general, including for instance also discrete distributions. The rate of convergence under alternatives is determined both when comparing the minimum contrast estimator with a "natural" parameter in the parameter space and when comparing it with the proposed "true" value in the parameter space. It turns out that under the model the asymptotic optimality of the maximum likelihood estimator in the local sense continues to hold in the moderate deviation area

Conditionally externally Bayesian pooling operators in chain graphs

We address the multivariate version of French’s group decision problem where the m members of a group, who are jointly responsible for the decisions they should make, wish to combine their beliefs about the possible values of n random variables into the group consensus probability distribution. We shall assume the group has agreed on the structure of associations of variables in a problem, as might be represented by a commonly agreed partially complete chain graph (PCG) we define in the paper. However, the members diverge about the actual conditional probability distributions for the variables in the common PCG. The combination algorithm we suggest they adopt is one which demands, at least on learning information which is common to the members and which preserves the originally agreed PCG structure, that the pools of conditional distributions associated with the PCG are externally Bayesian (EB). We propose a characterization for such conditionally EB (CEB) poolings which is more general and flexible than the characterization proposed by Genest, McConway and Schervish. In particular, such a generalization allows the weights attributed to the joint probability assessments of different individuals in the pool to differ across the distinct components of each joint density. We show that the group’s commitment to being CEB on chain elements can be accomplished by the group being EB on the whole PCG when the group also agrees to perform the conditional poolings in an ordering compatible with evidence propagation in the graph

Size and power of pretest procedures

A pre-test procedure consists of a preliminary test on a nuisance parameter, investigating whether it equals a given value or not, followed by the main testing problem on the parameter of interest. In case of acceptance of the preliminary test, the main test is applied in the restricted family with the given value of the nuisance parameter, while otherwise the test is performed in the complete family, including the nuisance parameter. For locally most powerful tests an attractive expression for the size and power of the pre-test procedure is derived using second order asymptotics. From this expression considerable insight can be obtained in a qualitative and quantitative sense. The results can be applied easily as is illustrated by a number of practical examples, where also the accuracy of the approximations is seen from comparison with numerical results

A General-Theory Of Asymptotic Consistency For Subset-Selection With Applications

Mathematic

Behavioral, computational, and neuroimaging studies of acquired apraxia of speech

A critical examination of speech motor control depends on an in-depth understanding of network connectivity associated with Brodmann areas 44 and 45 and surrounding cortices. Damage to these areas has been associated with two conditions-the speech motor programming disorder apraxia of speech (AOS) and the linguistic/grammatical disorder of Broca's aphasia. Here we focus on AOS, which is most commonly associated with damage to posterior Broca's area (BA) and adjacent cortex. We provide an overview of our own studies into the nature of AOS, including behavioral and neuroimaging methods, to explore components of the speech motor network that are associated with normal and disordered speech motor programming in AOS. Behavioral, neuroimaging, and computational modeling studies are indicating that AOS is associated with impairment in learning feedforward models and/or implementing feedback mechanisms and with the functional contribution of BA6. While functional connectivity methods are not yet routinely applied to the study of AOS, we highlight the need for focusing on the functional impact of localized lesions throughout the speech network, as well as larger scale comparative studies to distinguish the unique behavioral and neurological signature of AOS. By coupling these methods with neural network models, we have a powerful set of tools to improve our understanding of the neural mechanisms that underlie AOS, and speech production generally