Reversing Statistical Erasure of Indigenous Peoples

2021 ◽  
Author(s):  
Kimberly R. Huyser ◽  
Sofia Locklear
Keyword(s):  
Indigenous Peoples ◽  
Alaska Native ◽  
National Level ◽  
Large Data ◽  
The United States ◽  
Large Data Sets ◽  
Data Sets ◽  
First Peoples ◽  
Survey Instruments ◽  
Disaggregated Data

American Indian and Alaska Native (AIAN) Peoples are diverse, but their diversity is statistically flattened in national-level survey data and, subsequently, in contemporary understandings of race and inequality in the United States. This chapter demonstrates the utility of disaggregated data for gaining, for instance, nuanced information on social outcomes such as educational attainment and income levels, and shaping resource allocation accordingly. Throughout, it explores both reasons and remedies for AIAN invisibility in large data sets. Using their personal identities as a case in point, the authors argue for more refined survey instruments, informed by Indigenous modes of identity and affiliation, not only to raise the statistical salience of AIANs but also to paint a fuller picture of a vibrant, heterogeneous First Peoples all too often dismissed as a vanishing people.

The Victim's Experience and Fear of Crime

1998 ◽  
Vol 5 (2) ◽  
pp. 93-140 ◽  
Author(s):  
Helmut Kury ◽  
Theodore Ferdinand
Keyword(s):  
United States ◽  
Fear Of Crime ◽  
Rapid Development ◽  
Large Data ◽  
The United States ◽  
Fundamental Concept ◽  
Large Data Sets ◽  
Data Sets ◽  
Free Society ◽  
Public Concern

With the rapid development of sophisticated victim surveys, the fear of crime has emerged as a fundamental concept in theoretical and practical discourse. Since publication of the Report of the President's Commission The Challenge of Crime in a Free Society (1967), the fear of offenders has become a major public concern in the United States alongside the mounting problem of crime itself. The flourishing of national crime surveys in the United States and in Europe has in turn led to large data sets examining carefully not only the knowledge and experience of the victims regarding criminality but also the fear of offenders and its causes ( cf. Herbert and Darwood, 1992; p. 145). We shall offer first, a review of research on these issues in Europe and the United States, and then we shall report our research that has probed these issues in a focused manner.

scholarly journals An Analytical Model for Evaluating Social Security Schemes-A Focus on “Ayushman Bharat” Universal Health Scheme in India

2019 ◽  
Vol 8 (3) ◽  
pp. 8929-8936
Keyword(s):  
Data Mining ◽  
Social Security ◽  
National Level ◽  
Large Data ◽  
Large Data Sets ◽  
Data Sets ◽  
Financial Decision ◽  
Frame Work ◽  
The Government ◽  
Time Basis

The government initiated social security schemes in countries such as India, target a large proportion of the population to provide various types of benefits that involve a number of stakeholders. Such schemes are executed by a large number of transactions between the Government agencies and the other stakeholders on a real time basis, thus resulting in large data sets. Current research advancements in the domain of social security schemes include analysis of sequential activities and debt occurrences for such transactions at the national level only. It has been a challenge in recent times to monitor and evaluate the performance of such gigantic schemes which also involves financial decision making at different levels. This paper proposes an innovative frame-work that combines data mining strategies with actuarial techniques to evaluate one of the popular schemes in India, namely AB-PMJAY (“Ayushman Bharat–Pradhan Mantri Jan Arogya Yojana”) launched by the Government in 2018 at family level. In the proposed framework, the scheme has been divided into a number of sub-processes for which various data mining techniques such as, clustering, classification, anomaly detection and actuarial techniques for pricing are proposed to evaluate the scheme effective at micro level

scholarly journals To the New World and Back Again: Return Migrants in the Age of Mass Migration

ILR Review ◽  
2017 ◽  
Vol 72 (2) ◽  
pp. 300-322 ◽  
Author(s):  
Ran Abramitzky ◽  
Leah Boustan ◽  
Katherine Eriksson
Keyword(s):  
United States ◽  
Return Migration ◽  
New World ◽  
Large Data ◽  
The United States ◽  
Large Data Sets ◽  
Data Sets ◽  
Return Migrants ◽  
Mass Migration ◽  
Before And After

The authors compile large data sets from Norwegian and US historical censuses to study return migration during the Age of Mass Migration (1850–1913). Norwegian immigrants who returned to Norway held lower-paid occupations than did Norwegian immigrants who stayed in the United States, both before and after their first transatlantic migration, suggesting they were negatively selected from the migrant pool. Upon returning to Norway, return migrants held higher-paid occupations relative to Norwegians who never moved, despite hailing from poorer backgrounds. These patterns suggest that despite being negatively selected, return migrants had been able to accumulate savings and could improve their economic circumstances once they returned home.

Crop response to nitrogen fertilizer: The delta yield concept

2009 ◽  
Vol 89 (5) ◽  
pp. 543-554 ◽  
Author(s):  
R G Kachanoski
Keyword(s):  
Large Data ◽  
Field Studies ◽  
The United States ◽  
Large Data Sets ◽  
Yield Response ◽  
Data Sets ◽  
Price Ratio ◽  
Response Functions ◽  
Fertilizer N ◽  
Yield Increase

Estimation of fertilizer N requirements of crops remains a challenge. Numerous field studies have been carried out to calibrate soil tests against yield response to applied fertilizer N. Synthesis and identification of common crop fertilizer N response across large data sets (years, sites) will allow maximum use of this past work and a framework for comparison of future work. The objective of this paper is to define macro-relationships between the economically optimum fertilizer N rate (EONR) and the yield increase at the EONR defined as the delta yield, ΔYec , for large data sets of 2nd- and 3rd-order estimates of fertilizer N response functions with both 0th and 1st-order rate relationships between fertilizer nitrogen use efficiency and applied fertilizer N. The derived macro-relationships are curvilinear, depend on the price ratio R = the ratio of the (price per kilogram of fertilizer N)/(price per kilogram grain), and are similar to measurements from data sets of corn fertilizer N response functions spanning decades (+20 yr) and representing areas in both the United States and Canada. The macro-relationships appear to be robust and therefore useful for quantifying (post-harvest analysis) soil fertility, crop fertilizer N requirement, and comparison/classification of N response functions.Key words: Response function, prediction, efficiency, economic N rate, corn

An example of spectrum imaging used for comparison of EELS quantitative analysis techniques on Al-Li

1991 ◽  
Vol 49 ◽  
pp. 726-727
Author(s):  
John A. Hunt
Keyword(s):  
Quantitative Analysis ◽  
Large Data ◽  
Difference Spectrum ◽  
Large Data Sets ◽  
Foil Thickness ◽  
Data Sets ◽  
Analysis Techniques ◽  
Spectrum Imaging ◽  
Normal Spectrum ◽  
Electron Energy Loss

Spectrum-imaging is a useful technique for comparing different processing methods on very large data sets which are identical for each method. This paper is concerned with comparing methods of electron energy-loss spectroscopy (EELS) quantitative analysis on the Al-Li system. The spectrum-image analyzed here was obtained from an Al-10at%Li foil aged to produce δ' precipitates that can span the foil thickness. Two 1024 channel EELS spectra offset in energy by 1 eV were recorded and stored at each pixel in the 80x80 spectrum-image (25 Mbytes). An energy range of 39-89eV (20 channels/eV) are represented. During processing the spectra are either subtracted to create an artifact corrected difference spectrum, or the energy offset is numerically removed and the spectra are added to create a normal spectrum. The spectrum-images are processed into 2D floating-point images using methods and software described in [1].

Cluster analysis for large data sets: applications to individual aerosol particles from the mid-pacific

1992 ◽  
Vol 50 (2) ◽  
pp. 1488-1489
Author(s):  
Thomas W. Shattuck ◽  
James R. Anderson ◽  
Neil W. Tindale ◽  
Peter R. Buseck
Keyword(s):  
Cluster Analysis ◽  
Chemical Reactivity ◽  
Large Data ◽  
Large Data Sets ◽  
Particle Analysis ◽  
Data Sets ◽  
Halogen Chemistry ◽  
Complete Study ◽  
Components Analysis ◽  
Automated Scanning

Individual particle analysis involves the study of tens of thousands of particles using automated scanning electron microscopy and elemental analysis by energy-dispersive, x-ray emission spectroscopy (EDS). EDS produces large data sets that must be analyzed using multi-variate statistical techniques. A complete study uses cluster analysis, discriminant analysis, and factor or principal components analysis (PCA). The three techniques are used in the study of particles sampled during the FeLine cruise to the mid-Pacific ocean in the summer of 1990. The mid-Pacific aerosol provides information on long range particle transport, iron deposition, sea salt ageing, and halogen chemistry.Aerosol particle data sets suffer from a number of difficulties for pattern recognition using cluster analysis. There is a great disparity in the number of observations per cluster and the range of the variables in each cluster. The variables are not normally distributed, they are subject to considerable experimental error, and many values are zero, because of finite detection limits. Many of the clusters show considerable overlap, because of natural variability, agglomeration, and chemical reactivity.

NVESTIGATION OF THE EFFICIENCY OF DISTRIBUTED INFORMATION SYSTEMS BASED ON THE PROCESSING OF LARGE AMOUNTS OF DATA

2019 ◽  
Author(s):  
Mykhajlo Klymash ◽  
Olena Hordiichuk — Bublivska ◽  
Ihor Tchaikovskyi ◽  
Oksana Urikova
Keyword(s):  
Distributed Systems ◽  
Large Data ◽  
Large Data Sets ◽  
Data Sets ◽  
Data Decomposition ◽  
Distributed Information ◽  
Software Model ◽  
Computing Performance ◽  
Mapreduce Model ◽  
Singular Data

In this article investigated the features of processing large arrays of information for distributed systems. A method of singular data decomposition is used to reduce the amount of data processed, eliminating redundancy. Dependencies of com­putational efficiency on distributed systems were obtained using the MPI messa­ging protocol and MapReduce node interaction software model. Were analyzed the effici­ency of the application of each technology for the processing of different sizes of data: Non — distributed systems are inefficient for large volumes of information due to low computing performance. It is proposed to use distributed systems that use the method of singular data decomposition, which will reduce the amount of information processed. The study of systems using the MPI protocol and MapReduce model obtained the dependence of the duration calculations time on the number of processes, which testify to the expediency of using distributed computing when processing large data sets. It is also found that distributed systems using MapReduce model work much more efficiently than MPI, especially with large amounts of data. MPI makes it possible to perform calculations more efficiently for small amounts of information. When increased the data sets, advisable to use the Map Reduce model.

Using Large Data Sets and Statistical Tools to Define “Failure” and Demonstrate the Safety of DPR

2018 ◽  
Vol 2018 (6) ◽  
pp. 38-39
Author(s):  
Austa Parker ◽  
Yan Qu ◽  
David Hokanson ◽  
Jeff Soller ◽  
Eric Dickenson ◽  
...  
Keyword(s):  
Large Data ◽  
Large Data Sets ◽  
Data Sets ◽  
Statistical Tools

scholarly journals Online Judging Platform Utilizing Dynamic Plagiarism Detection Facilities

Computers ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 47
Author(s):  
Fariha Iffath ◽  
A. S. M. Kayes ◽  
Md. Tahsin Rahman ◽  
Jannatul Ferdows ◽  
Mohammad Shamsul Arefin ◽  
...  
Keyword(s):  
Source Code ◽  
Large Data ◽  
Large Data Sets ◽  
Detection Technique ◽  
Data Sets ◽  
Plagiarism Detection ◽  
Source Codes ◽  
Efficient Detection ◽  
Mathematical Problems ◽  
Automatic Scoring

A programming contest generally involves the host presenting a set of logical and mathematical problems to the contestants. The contestants are required to write computer programs that are capable of solving these problems. An online judge system is used to automate the judging procedure of the programs that are submitted by the users. Online judges are systems designed for the reliable evaluation of the source codes submitted by the users. Traditional online judging platforms are not ideally suitable for programming labs, as they do not support partial scoring and efficient detection of plagiarized codes. When considering this fact, in this paper, we present an online judging framework that is capable of automatic scoring of codes by detecting plagiarized contents and the level of accuracy of codes efficiently. Our system performs the detection of plagiarism by detecting fingerprints of programs and using the fingerprints to compare them instead of using the whole file. We used winnowing to select fingerprints among k-gram hash values of a source code, which was generated by the Rabin–Karp Algorithm. The proposed system is compared with the existing online judging platforms to show the superiority in terms of time efficiency, correctness, and feature availability. In addition, we evaluated our system by using large data sets and comparing the run time with MOSS, which is the widely used plagiarism detection technique.

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