Professional Networks and Street-Level Performance

2016 ◽  
Vol 47 (1) ◽  
pp. 79-101 ◽  
Author(s):  
Michael D. Siciliano

Given the complexity of their work, street-level bureaucrats rely on their professional networks to access implementation resources and information. Despite the acknowledged importance of these networks, little research exists on how network structure and composition influence frontline performance. This study analyzes a unique data set that includes the professional networks of more than 420 teachers in 21 public schools along with 3 years of administrative data on student test scores and student demographics. Using value-added models derived from the student test data, objective measures of teacher performance were calculated. The results suggest that street-level performance is influenced by both network structure and composition. Thus, the actions of street-level workers are not independent responses to individual dilemmas, but rather are developed and shaped by specific features of the social structure in which the individual bureaucrat is embedded.

2019 ◽  
Vol 16 (2) ◽  
pp. 445-452
Author(s):  
Kishore S. Verma ◽  
A. Rajesh ◽  
Adeline J. S. Johnsana

K anonymization is one of the worldwide used approaches to protect the individual records from the privacy leakage attack of Privacy Preserving Data Mining (PPDM) arena. Typically anonymized dataset will impact the effectiveness of data mining results. Anyhow, currently researchers of PPDM progress in driving their efforts in finding out the optimum trade-off between privacy and utility. This work tends in bringing out the optimum classifier from a set of best classifiers of data mining approaches that are capable enough in generating value-added classifying results on utility aware k-anonymized data set. We performed the analytical approach on the data set that are anonymized in sense of accompanying the anonymity utility factors like null values count and transformation pattern loss. The experimentation is done with three widely used classifiers HNB, PART and J48 and these classifiers are analysed with Accuracy, F-measure, and ROC-AUC which are literately proved to be the perfect measures of classification. Our experimental analysis reveals the best classifiers on the utility aware anonymized data sets of Cell oriented Anonymization (CoA), Attribute oriented Anonymization (AoA) and Record oriented Anonymization (RoA).


Author(s):  
D. E. Becker

An efficient, robust, and widely-applicable technique is presented for computational synthesis of high-resolution, wide-area images of a specimen from a series of overlapping partial views. This technique can also be used to combine the results of various forms of image analysis, such as segmentation, automated cell counting, deblurring, and neuron tracing, to generate representations that are equivalent to processing the large wide-area image, rather than the individual partial views. This can be a first step towards quantitation of the higher-level tissue architecture. The computational approach overcomes mechanical limitations, such as hysterisis and backlash, of microscope stages. It also automates a procedure that is currently done manually. One application is the high-resolution visualization and/or quantitation of large batches of specimens that are much wider than the field of view of the microscope.The automated montage synthesis begins by computing a concise set of landmark points for each partial view. The type of landmarks used can vary greatly depending on the images of interest. In many cases, image analysis performed on each data set can provide useful landmarks. Even when no such “natural” landmarks are available, image processing can often provide useful landmarks.


2020 ◽  

BACKGROUND: This paper deals with territorial distribution of the alcohol and drug addictions mortality at a level of the districts of the Slovak Republic. AIM: The aim of the paper is to explore the relations within the administrative territorial division of the Slovak Republic, that is, between the individual districts and hence, to reveal possibly hidden relation in alcohol and drug mortality. METHODS: The analysis is divided and executed into the two fragments – one belongs to the female sex, the other one belongs to the male sex. The standardised mortality rate is computed according to a sequence of the mathematical relations. The Euclidean distance is employed to compute the similarity within each pair of a whole data set. The cluster analysis examines is performed. The clusters are created by means of the mutual distances of the districts. The data is collected from the database of the Statistical Office of the Slovak Republic for all the districts of the Slovak Republic. The covered time span begins in the year 1996 and ends in the year 2015. RESULTS: The most substantial point is that the Slovak Republic possesses the regional disparities in a field of mortality expressed by the standardised mortality rate computed particularly for the diagnoses assigned to the alcohol and drug addictions at a considerably high level. However, the female sex and the male sex have the different outcome. The Bratislava III District keeps absolutely the most extreme position. It forms an own cluster for the both sexes too. The Topoľčany District bears a similar extreme position from a point of view of the male sex. All the Bratislava districts keep their mutual notable dissimilarity. Contrariwise, evaluation of a development of the regional disparities among the districts looks like notably heterogeneously. CONCLUSIONS: There are considerable regional discrepancies throughout the districts of the Slovak Republic. Hence, it is necessary to create a common platform how to proceed with the solution of this issue.


Author(s):  
Andrea M. Leiter ◽  
Engelbert Theurl

AbstractIn this paper we examine determinants of prepaid modes of health care financing in a worldwide cross-country perspective. We use three different indicators to capture the role of prepaid modes in health care financing: (i) the share of total prepaid financing as percent of total current health expenditures, (ii) the share of voluntary prepaid financing as percent of total prepaid financing, and (iii) the share of compulsory health insurance as percent of total compulsory prepaid financing. In the econometric analysis, we refer to a panel data set comprising 154 countries and covering the time period 2000–2015. We apply a static as well as a dynamic panel data model. We find that the current structure of prepaid financing is significantly determined by its different forms in the past. The significant influence of GDP per capita, governmental revenues, the agricultural value added, development assistance for health, degree of urbanization and regulatory quality varies depending on the financing structure we look at. The share of the elderly and the education level are only of minor importance for explaining the variation in a country’s share of prepaid health care financing. The importance of the mentioned variables as determinants for prepaid health care financing also varies depending on the countries’ socio-economic development. From our analysis we conclude that more detailed information on indicators which reflect the distribution of individual characteristics (such as income, family size and structure and health risks) within a country’s population would be needed to gain deeper insight into the decisive determinants for prepaid health care financing.


2020 ◽  
pp. 003329412097815
Author(s):  
Giovanni Briganti ◽  
Donald R. Williams ◽  
Joris Mulder ◽  
Paul Linkowski

The aim of this work is to explore the construct of autistic traits through the lens of network analysis with recently introduced Bayesian methods. A conditional dependence network structure was estimated from a data set composed of 649 university students that completed an autistic traits questionnaire. The connectedness of the network is also explored, as well as sex differences among female and male subjects in regard to network connectivity. The strongest connections in the network are found between items that measure similar autistic traits. Traits related to social skills are the most interconnected items in the network. Sex differences are found between female and male subjects. The Bayesian network analysis offers new insight on the connectivity of autistic traits as well as confirms several findings in the autism literature.


2020 ◽  
pp. 1-11
Author(s):  
Jie Liu ◽  
Hongbo Zhao

BACKGROUND: Convolution neural network is often superior to other similar algorithms in image classification. Convolution layer and sub-sampling layer have the function of extracting sample features, and the feature of sharing weights greatly reduces the training parameters of the network. OBJECTIVE: This paper describes the improved convolution neural network structure, including convolution layer, sub-sampling layer and full connection layer. This paper also introduces five kinds of diseases and normal eye images reflected by the blood filament of the eyeball “yan.mat” data set, convenient to use MATLAB software for calculation. METHODSL: In this paper, we improve the structure of the classical LeNet-5 convolutional neural network, and design a network structure with different convolution kernels, different sub-sampling methods and different classifiers, and use this structure to solve the problem of ocular bloodstream disease recognition. RESULTS: The experimental results show that the improved convolutional neural network structure is ideal for the recognition of eye blood silk data set, which shows that the convolution neural network has the characteristics of strong classification and strong robustness. The improved structure can classify the diseases reflected by eyeball bloodstain well.


Author(s):  
Shaoqiang Wang ◽  
Shudong Wang ◽  
Song Zhang ◽  
Yifan Wang

Abstract To automatically detect dynamic EEG signals to reduce the time cost of epilepsy diagnosis. In the signal recognition of electroencephalogram (EEG) of epilepsy, traditional machine learning and statistical methods require manual feature labeling engineering in order to show excellent results on a single data set. And the artificially selected features may carry a bias, and cannot guarantee the validity and expansibility in real-world data. In practical applications, deep learning methods can release people from feature engineering to a certain extent. As long as the focus is on the expansion of data quality and quantity, the algorithm model can learn automatically to get better improvements. In addition, the deep learning method can also extract many features that are difficult for humans to perceive, thereby making the algorithm more robust. Based on the design idea of ResNeXt deep neural network, this paper designs a Time-ResNeXt network structure suitable for time series EEG epilepsy detection to identify EEG signals. The accuracy rate of Time-ResNeXt in the detection of EEG epilepsy can reach 91.50%. The Time-ResNeXt network structure produces extremely advanced performance on the benchmark dataset (Berne-Barcelona dataset) and has great potential for improving clinical practice.


PEDIATRICS ◽  
1989 ◽  
Vol 84 (1) ◽  
pp. 93-93
Author(s):  
T. E. C.

During the mid-nineteenth century American physicians were greatly troubled by what they thought were the evils of excessive academic demands placed on children in our public schools. The editorial below, published in 1854 in the Boston Medical and Surgical Journal, is typical of many of a similar nature. Our city prides itself on the superiority of its public schools; and we think Boston is justly entitled to take the highest rank among the cities of the civilized world for the facilities afforded by its citizens for the education of youth. But notwithstanding the large expenditure of money for the erection of beautiful and commodious school-houses, for mathematical and other instruments, for teachers, &c., all which give a character to our Boston schools, there exists an evil in the present system of educating, which seriously demands attention, and, if possible, a remedy. It is the ambition of the teachers of our schools, to have their scholars thoroughly instructed, and that they may appear well before the committees at examinations; and for that purpose, lessons in great numbers and requiring toilsome study, are imposed upon them. No discrimination is made, as regards the mental or physical capacity of the individual members of the class, but all are required to be perfect in their answers, or else they lose their position in the class and school. Not one fifth of the time devoted to school hours is allowed for study, being occupied in recitations; and the severe tasks the poor children have in getting their lessons must be apparent, when it is known that so long a time is required in reciting them. The scholars of the second class, for instance, have to commit to memory from twelve to twenty-five pages of geography, three to six pages of arithmetic, the same of grammar, three pages in spelling, besides exercises in reading, writing, &c. Now these lessons must be studied out of school, at the time which should be devoted to exercise and recreation. The imposition of such severe tasks upon the young and growing children, must enfeeble their constutions, and often incapacitates them, if they arrive at maturity, for enjoying life. We have seen many children who were ambitious to accomplish all that was required of them by teachers; and to do so, the greatest portion of the twenty-four hours was necessarily devoted to their books, scarcely allowing any time for taking their meals. It must be obvious to every one, that such close application to study, produces, in their turn, a train of diseases which cannot always be eradicated. Aching heads, loss of appetite, sleepless nights, inflamed eyes, with other deviations from health, are the accompaniments and the consequences of excessive mental exertion.


2021 ◽  
Vol 123 (6) ◽  
pp. 1-38
Author(s):  
Debbie H. Kim ◽  
Kelly Krupa Rifelj

Background Promise programs are a quickly spreading policy tool in the free college movement. Despite their rapid spread, promise programs remain generally untested and there is even less information about how they are implemented. Research Questions (1) In what ways were The Degree Project's (TDP) theory of change and intents represented in messaging materials to students and to school staff? 1(a) In what ways did these messages shape conditions (or not) for sensemaking? (2) In what ways did these messages support (or not) students and school staff in changing their practice? (2a) What changes in practice did we see (or not) for students and school staff? Intervention TDP, which was implemented in Milwaukee Public Schools between 2011–15, is the nation's first randomized control trial of a promise program. Freshmen in the treatment group were offered $12,000 for college if they met particular requirements (e.g., average 2.5 GPA, 90% attendance). TDP leaned heavily on marketing materials and personalized letters to students, families, and school staff to communicate its requirements and to provide college access tips. Research Design We analyze messaging materials, climate and exit survey data, and student and school staff interviews to understand how TDP's theory of change and intents were packaged into messaging materials and ultimately enacted among target students and staff. Findings TDP implementation was successful to a point. School staff handed out messaging materials; students understood the requirements and demonstrated an increase in motivation and desire to go to college. However, TDP failed to meet its goal of sending more students to college. Expectations for school staff (hand out flyers and speak to students) versus students were misaligned, contributing to a lack of substantive conversation and structures for students to convert their increased motivation to go to college into actionable practices over time. School staff were already stretched thin and, with no added structural support, were unable to interact more meaningfully with students. Conclusion TDP failed to send more students to college because it targeted change at the individual rather than organizational level. Students exhibited change in their motivation to attend college, but this was not met with the support needed to convert this motivation to meaningful action. To achieve their full potential, such programs will have to not only address financial barriers, but also leverage broader structural supports in schools to help channel increased student motivation in more productive directions.


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