scholarly journals Word embeddings quantify 100 years of gender and ethnic stereotypes

2018 ◽  
Vol 115 (16) ◽  
pp. E3635-E3644 ◽  
Author(s):  
Nikhil Garg ◽  
Londa Schiebinger ◽  
Dan Jurafsky ◽  
James Zou
Keyword(s):  
United States ◽  
Machine Learning ◽  
Temporal Dynamics ◽  
Temporal Analysis ◽  
The United States ◽  
English Word ◽  
Attitudes Toward Women ◽  
Word Embeddings ◽  
Asian Immigration ◽  
Over Time

Word embeddings are a powerful machine-learning framework that represents each English word by a vector. The geometric relationship between these vectors captures meaningful semantic relationships between the corresponding words. In this paper, we develop a framework to demonstrate how the temporal dynamics of the embedding helps to quantify changes in stereotypes and attitudes toward women and ethnic minorities in the 20th and 21st centuries in the United States. We integrate word embeddings trained on 100 y of text data with the US Census to show that changes in the embedding track closely with demographic and occupation shifts over time. The embedding captures societal shifts—e.g., the women’s movement in the 1960s and Asian immigration into the United States—and also illuminates how specific adjectives and occupations became more closely associated with certain populations over time. Our framework for temporal analysis of word embedding opens up a fruitful intersection between machine learning and quantitative social science.

Gender-Role Attitudes in University Students in the United States, Slovenia, and Croatia

2003 ◽  
Vol 27 (3) ◽  
pp. 256-261 ◽  
Author(s):  
Irene Hanson Frieze ◽  
Anuš;ka Ferligoj ◽  
Tina Kogovšek ◽  
Tanja Rener ◽  
Jasna Horvat ◽  
...  
Keyword(s):  
United States ◽  
University Students ◽  
Gender Role ◽  
Gender Role Attitudes ◽  
The United States ◽  
Attitudes Toward Women ◽  
Sexist Attitudes ◽  
Frequent Attendance ◽  
Religious Services ◽  
Over Time

Determinants of gender-role attitudes were examined in samples of university students from Pittsburgh in the United States, Ljubljana in Slovenia, and Osijek in Croatia. Surveys including items from the Attitudes Toward Women Scale and the Neosexism Scale were administered to a total of 1,544 U.S. students, 912 Slovene students, and 996 Croatian students between the years of 1991 and 2000. As predicted, men held less egalitarian or more sexist attitudes about the appropriate roles for women and men, and those with more frequent attendance at religious services held more sexist attitudes. No changes in attitudes were found for women over time, but Slovene males were found to become more traditional over time.

Spatial and Temporal Dynamics of Social Vulnerability in the United States from 1970 to 2010

2020 ◽  
Vol 11 (1) ◽  
pp. 36-54
Author(s):  
Gainbi Park ◽  
Zengwang Xu
Keyword(s):  
United States ◽  
Social Vulnerability ◽  
Temporal Dynamics ◽  
The United States ◽  
Human Society ◽  
Social Vulnerability Index ◽  
Frequent Change ◽  
Spatial And Temporal Dynamics ◽  
The Social ◽  
Over Time

Social vulnerability has been an important concept to characterize the extent to which human society is vulnerable to hazards. Although it is well known that social vulnerability varies across space and over time, there is only a paucity of studies to examine the basic patterns of the spatial and temporal dynamics of the social vulnerability in the United States. This study examines the spatial and temporal dynamics of social vulnerability of the U.S. counties from 1970 to 2010. For each decade, social vulnerability of counties is quantified by the social vulnerability index (SoVI) using county-level social, economic, demographic, and built environment characteristics. The SoVI is mainly designed to quantify the cross-sectional variation of social vulnerability and is not conducive to direct comparison over time. This study implements a methodology that integrates quantile standardization, sequence alignment analysis, and cluster analysis to investigate how social vulnerability of U.S. counties has changed over time. The authors find that U.S. counties exhibit distinctive spatial and longitudinal patterns, and there are counties/areas which have persistent high or low social vulnerability as well as frequent change in their social vulnerability over time. The results can be useful for policymakers, disaster managers, planning officials, and social scientists in general.

scholarly journals Intelligent Drought Tracking for its Use in Machine Learning: Implementation and First Results

10.29007/klgg ◽  
2018 ◽  
Author(s):  
Vitali Diaz ◽  
Gerald A. Corzo Perez ◽  
Henny A.J. Van Lanen ◽  
Dimitri Solomatine
Keyword(s):  
Machine Learning ◽  
Temporal Dynamics ◽  
Temporal Analysis ◽  
Drought Monitoring ◽  
Grid Data ◽  
Drought Prediction ◽  
Documented Information ◽  
First Results ◽  
Spatio Temporal ◽  
Over Time

Due to the underlying characteristics of drought, monitoring of its spatio-temporal development is difficult. Last decades, drought monitoring have been increasingly developed, however, including its spatio-temporal dynamics is still a challenge. This study proposes a method to monitor drought by tracking its spatial extent. A methodology to build drought trajectories is introduced, which is put in the framework of machine learning (ML) for drought prediction. Steps for trajectories calculation are (1) spatial areas computation, (2) centroids localization, and (3) centroids linkage. The spatio- temporal analysis performed here follows the Contiguous Drought Area (CDA) analysis. The methodology is illustrated using grid data from the Standardized Precipitation Evaporation Index (SPEI) Global Drought Monitor over India (1901-2013), as an example. Results show regions where drought with considerable coverage tend to occur, and suggest possible concurrent routes. Tracks of six of the most severe reported droughts were analysed. In all of them, areas overlap considerably over time, which suggest that drought remains in the same region for a period of time. Years with the largest drought areas were 2000 and 2002, which coincide with documented information presented. Further research is under development to setup the ML model to predict the track of drought.

An Analysis of Fraudulent Qui Tam Actions Over Time in the United States

2007 ◽  
Author(s):  
Karen A. Fitzner ◽  
Charlie Bennett ◽  
June McKoy ◽  
Cara Tigue
Keyword(s):  
United States ◽  
The United States ◽  
Over Time

The New Economic Populism and the Future of Inequality in the United States

2017 ◽  
Author(s):  
William W. Franko ◽  
Christopher Witko
Keyword(s):  
United States ◽  
Federal Government ◽  
Labor Unions ◽  
Republican Party ◽  
The United States ◽  
The Public ◽  
Empirical Results ◽  
Income Disparities ◽  
The Face ◽  
Over Time

The authors conclude the book by recapping their arguments and empirical results, and discussing the possibilities for the “new economic populism” to promote egalitarian economic outcomes in the face of continuing gridlock and the dominance of Washington, DC’s policymaking institutions by business and the wealthy, and a conservative Republican Party. Many states are actually addressing inequality now, and these policies are working. Admittedly, many states also continue to embrace the policies that have contributed to growing inequality, such as tax cuts for the wealthy or attempting to weaken labor unions. But as the public grows more concerned about inequality, the authors argue, policies that help to address these income disparities will become more popular, and policies that exacerbate inequality will become less so. Over time, if history is a guide, more egalitarian policies will spread across the states, and ultimately to the federal government.

scholarly journals Descriptors of Cytochrome Inhibitors and Useful Machine Learning Based Methods for the Design of Safer Drugs

2021 ◽  
Vol 14 (5) ◽  
pp. 472
Author(s):  
Tyler C. Beck ◽  
Kyle R. Beck ◽  
Jordan Morningstar ◽  
Menny M. Benjamin ◽  
Russell A. Norris
Keyword(s):  
United States ◽  
Machine Learning ◽  
Drug Interactions ◽  
The United States ◽  
Structural Features ◽  
Physiochemical Properties ◽  
Drug Dosing ◽  
Therapeutic Outcomes ◽  
Cyp Inhibition ◽  
Cyp Inhibitors

Roughly 2.8% of annual hospitalizations are a result of adverse drug interactions in the United States, representing more than 245,000 hospitalizations. Drug–drug interactions commonly arise from major cytochrome P450 (CYP) inhibition. Various approaches are routinely employed in order to reduce the incidence of adverse interactions, such as altering drug dosing schemes and/or minimizing the number of drugs prescribed; however, often, a reduction in the number of medications cannot be achieved without impacting therapeutic outcomes. Nearly 80% of drugs fail in development due to pharmacokinetic issues, outlining the importance of examining cytochrome interactions during preclinical drug design. In this review, we examined the physiochemical and structural properties of small molecule inhibitors of CYPs 3A4, 2D6, 2C19, 2C9, and 1A2. Although CYP inhibitors tend to have distinct physiochemical properties and structural features, these descriptors alone are insufficient to predict major cytochrome inhibition probability and affinity. Machine learning based in silico approaches may be employed as a more robust and accurate way of predicting CYP inhibition. These various approaches are highlighted in the review.

scholarly journals Thirty-Day Laboratory-Based Surveillance for Carbapenem-Resistant Enterobacteriaceae in the Minneapolis-St. Paul Metropolitan Area

2014 ◽  
Vol 35 (4) ◽  
pp. 423-425 ◽  
Author(s):  
Edwin C. Pereira ◽  
Kristin M. Shaw ◽  
Paula M. Snippes Vagnone ◽  
Jane E. Harper ◽  
Alexander J. Kallen ◽  
...  
Keyword(s):  
United States ◽  
Metropolitan Area ◽  
The United States ◽  
Carbapenem Resistant ◽  
Over Time ◽  
Carbapenem Resistant Enterobacteriaceae

Carbapenem-resistant Enterobacteriaceae (CRE) are a growing problem in the United States. We explored the feasibility of active laboratory-based surveillance of CRE in a metropolitan area not previously considered to be an area of CRE endemicity. We provide a framework to address CRE surveillance and to monitor changes in the incidence of CRE infection over time.

CIKM 2020 conference report

2021 ◽  
pp. 1-4
Author(s):  
Mathieu D'Aquin ◽  
Stefan Dietze
Keyword(s):  
United States ◽  
Machine Learning ◽  
Knowledge Management ◽  
Information Retrieval ◽  
Conference Report ◽  
The United States ◽  
Knowledge Based ◽  
The World ◽  
Science Conference ◽  
Information And Knowledge Management

The 29th ACM International Conference on Information and Knowledge Management (CIKM) was held online from the 19 th to the 23 rd of October 2020. CIKM is an annual computer science conference, focused on research at the intersection of information retrieval, machine learning, databases as well as semantic and knowledge-based technologies. Since it was first held in the United States in 1992, 28 conferences have been hosted in 9 countries around the world.

scholarly journals Revealing the spatial shifting pattern of COVID-19 pandemic in the United States

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Di Zhu ◽  
Xinyue Ye ◽  
Steven Manson
Keyword(s):  
United States ◽  
Long Range ◽  
Network Optimization ◽  
Network Modeling ◽  
The United States ◽  
Standard Series ◽  
Time And Space ◽  
Long Range Interactions ◽  
Dynamic Relationships ◽  
Over Time

AbstractWe describe the use of network modeling to capture the shifting spatiotemporal nature of the COVID-19 pandemic. The most common approach to tracking COVID-19 cases over time and space is to examine a series of maps that provide snapshots of the pandemic. A series of snapshots can convey the spatial nature of cases but often rely on subjective interpretation to assess how the pandemic is shifting in severity through time and space. We present a novel application of network optimization to a standard series of snapshots to better reveal how the spatial centres of the pandemic shifted spatially over time in the mainland United States under a mix of interventions. We find a global spatial shifting pattern with stable pandemic centres and both local and long-range interactions. Metrics derived from the daily nature of spatial shifts are introduced to help evaluate the pandemic situation at regional scales. We also highlight the value of reviewing pandemics through local spatial shifts to uncover dynamic relationships among and within regions, such as spillover and concentration among states. This new way of examining the COVID-19 pandemic in terms of network-based spatial shifts offers new story lines in understanding how the pandemic spread in geography.

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