scholarly journals Engaging STEM Ethics Education

2018 ◽  
Vol 4 ◽  
pp. 1
Author(s):  
Kelly Ann Joyce ◽  
Kendall Darfler ◽  
Dalton George ◽  
Jason Ludwig ◽  
Kristene Unsworth
Keyword(s):  
Data Science ◽  
Ethics Education ◽  
Social Contexts ◽  
Ethical Framework ◽  
Original Research ◽  
Educational Materials ◽  
Science Data ◽  
Social Scientists ◽  
Computer Scientists ◽  
Data Validity

The automation of knowledge via algorithms, code and big data has brought new ethical concerns that computer scientists and engineers are not yet trained to identify or mediate. We present our experience of using original research to develop scenarios to explore how STS scholars can produce materials that facilitate ethics education in computer science, data science, and software engineering. STS scholars are uniquely trained to investigate the societal context of science and technology as well as the meaning STEM researchers attach to their day-to-day work practices. In this project, we use a collaborative, co-constitutive method of doing ethics education that focuses on building an ethical framework based on empirical practices, highlighting two issues in particular: data validity and the relations between data and inequalities. Through data-grounded scenario writing, we demonstrate how STS scholars and other social scientists can apply their expertise to the production of educational materials to spark broad ranging discussions that explore the connections between values, ethics, STEM, politics, and social contexts.

scholarly journals Dialog Structure Through the Lens of Gender, Gender Environment, and Power

2017 ◽  
Vol 8 (2) ◽  
pp. 21-55
Author(s):  
Vinodkumar Prabhakaran ◽  
Owen Rambow
Keyword(s):  
Social Context ◽  
Human Behavior ◽  
Social Contexts ◽  
Social Scientists ◽  
Gender Information ◽  
The Social ◽  
Computer Scientists ◽  
Automatic Methods ◽  
And Gender ◽  
Textual Content

Understanding how the social context of an interaction affects our dialog behavior is of great interest to social scientists who study human behavior, as well as to computer scientists who build automatic methods to infer those social contexts. In this paper, we study the interaction of power, gender, and dialog behavior in organizational interactions. In order to perform this study, we first construct the Gender Identified Enron Corpus of emails, in which we semi-automatically assign the gender of around 23,000 individuals who authored around 97,000 email messages in the Enron corpus. This corpus, which is made freely available, is orders of magnitude larger than previously existing gender identified corpora in the email domain. Next, we use this corpus to perform a largescale data-oriented study of the interplay of gender and manifestations of power. We argue that, in addition to one’s own gender, the “gender environment” of an interaction, i.e., the gender makeup of one’s interlocutors, also affects the way power is manifested in dialog. We focus especially on manifestations of power in the dialog structure — both, in a shallow sense that disregards the textual content of messages (e.g., how often do the participants contribute, how often do they get replies etc.), as well as the structure that is expressed within the textual content (e.g., who issues requests and how are they made, whose requests get responses etc.). We find that both gender and gender environment affect the ways power is manifested in dialog, resulting in patterns that reveal the underlying factors. Finally, we show the utility of gender information in the problem of automatically predicting the direction of power between pairs of participants in email interactions.

scholarly journals A view from data science

2021 ◽  
Vol 8 (2) ◽  
pp. 205395172110401
Author(s):  
Anna Sapienza ◽  
Sune Lehmann
Keyword(s):  
Big Data ◽  
Data Science ◽  
Common Ground ◽  
Science Data ◽  
Social Scientists

For better and worse, our world has been transformed by Big Data. To understand digital traces generated by individuals, we need to design multidisciplinary approaches that combine social and data science. Data and social scientists face the challenge of effectively building upon each other’s approaches to overcome the limitations inherent in each side. Here, we offer a “data science perspective” on the challenges that arise when working to establish this interdisciplinary environment. We discuss how we perceive the differences and commonalities of the questions we ask to understand digital behaviors (including how we answer them), and how our methods may complement each other. Finally, we describe what a path toward common ground between these fields looks like when viewed from data science.

Computer and social scientists collaborate to solve social problems

2021 ◽  
Vol 49 (3) ◽  
pp. 13-13
Author(s):  
Taney Shondel
Keyword(s):  
New York ◽  
New York City ◽  
Criminal Justice ◽  
Data Science ◽  
Public Affairs ◽  
Social Scientists ◽  
The Us ◽  
Computer Scientists ◽  
Work Education ◽  
The City

When I have time to daydream, admittedly not often, I imagine the big New York City lawyer's office from Erin Brockovich. The room is huge and overlooks the city. Instead of lawyers around the massive table, there are computer scientists and social scientists. Specifically, Ph.D. candidates and their advisors from top programs in social work, education, criminal justice, public affairs, political science, and computer and data science from around the US.

scholarly journals Product formalisms for measures on spaces with binary tree structures: representation, visualization, and multiscale noise

2020 ◽  
Vol 8 ◽  
Author(s):  
Devasis Bassu ◽  
Peter W. Jones ◽  
Linda Ness ◽  
David Shallcross
Keyword(s):  
Data Science ◽  
Theoretical Foundation ◽  
Continuous Functions ◽  
Noise Model ◽  
Data Types ◽  
Data Set ◽  
Tree Structures ◽  
Simple Concept ◽  
Wide Range ◽  
Computer Scientists

Abstract In this paper, we present a theoretical foundation for a representation of a data set as a measure in a very large hierarchically parametrized family of positive measures, whose parameters can be computed explicitly (rather than estimated by optimization), and illustrate its applicability to a wide range of data types. The preprocessing step then consists of representing data sets as simple measures. The theoretical foundation consists of a dyadic product formula representation lemma, and a visualization theorem. We also define an additive multiscale noise model that can be used to sample from dyadic measures and a more general multiplicative multiscale noise model that can be used to perturb continuous functions, Borel measures, and dyadic measures. The first two results are based on theorems in [15, 3, 1]. The representation uses the very simple concept of a dyadic tree and hence is widely applicable, easily understood, and easily computed. Since the data sample is represented as a measure, subsequent analysis can exploit statistical and measure theoretic concepts and theories. Because the representation uses the very simple concept of a dyadic tree defined on the universe of a data set, and the parameters are simply and explicitly computable and easily interpretable and visualizable, we hope that this approach will be broadly useful to mathematicians, statisticians, and computer scientists who are intrigued by or involved in data science, including its mathematical foundations.

scholarly journals COVID-19 Evidence Accelerator: A parallel analysis to describe the use of Hydroxychloroquine with or without Azithromycin among hospitalized COVID-19 patients

PLoS ONE ◽  
2021 ◽  
Vol 16 (3) ◽  
pp. e0248128
Author(s):  
Mark Stewart ◽  
Carla Rodriguez-Watson ◽  
Adem Albayrak ◽  
Julius Asubonteng ◽  
Andrew Belli ◽  
...  
Keyword(s):  
Best Practices ◽  
Adverse Events ◽  
Data Science ◽  
Proportional Hazards ◽  
Cox Proportional Hazards ◽  
Parallel Analysis ◽  
Science Data ◽  
Systems Research ◽  
Cox Proportional Hazards Models ◽  
Global Threat

Background The COVID-19 pandemic remains a significant global threat. However, despite urgent need, there remains uncertainty surrounding best practices for pharmaceutical interventions to treat COVID-19. In particular, conflicting evidence has emerged surrounding the use of hydroxychloroquine and azithromycin, alone or in combination, for COVID-19. The COVID-19 Evidence Accelerator convened by the Reagan-Udall Foundation for the FDA, in collaboration with Friends of Cancer Research, assembled experts from the health systems research, regulatory science, data science, and epidemiology to participate in a large parallel analysis of different data sets to further explore the effectiveness of these treatments. Methods Electronic health record (EHR) and claims data were extracted from seven separate databases. Parallel analyses were undertaken on data extracted from each source. Each analysis examined time to mortality in hospitalized patients treated with hydroxychloroquine, azithromycin, and the two in combination as compared to patients not treated with either drug. Cox proportional hazards models were used, and propensity score methods were undertaken to adjust for confounding. Frequencies of adverse events in each treatment group were also examined. Results Neither hydroxychloroquine nor azithromycin, alone or in combination, were significantly associated with time to mortality among hospitalized COVID-19 patients. No treatment groups appeared to have an elevated risk of adverse events. Conclusion Administration of hydroxychloroquine, azithromycin, and their combination appeared to have no effect on time to mortality in hospitalized COVID-19 patients. Continued research is needed to clarify best practices surrounding treatment of COVID-19.

scholarly journals The National Sports and Society Survey Methodological Summary

2020 ◽  
Author(s):  
Chris Knoester ◽  
Elizabeth C. Cooksey
Keyword(s):  
Data Collection ◽  
Quantitative Research ◽  
Survey Design ◽  
Well Being ◽  
Social Contexts ◽  
Science Data ◽  
Social Patterns ◽  
Sports Involvement ◽  
The Ohio State University ◽  
Survey Responses

The 2018-19 National Sports and Society Survey (NSASS) was designed to be a unique social science data collection effort to enable quantitative research on important but under-researched issues. In particular, its intent was to provide comprehensive empirical evidence of sports involvement patterns and contexts, sports-related public opinions and ideologies, and indicators of well-being, social patterns, and the more general social contexts of adult Americans ages 21-65. Respondents were also asked to report on the sports involvement histories and contexts, family and school experiences, and health and well-being of a child between 6 and 17 years old if they had such a child who also shared a residence with them. A targeted sample of 4,000 adults was established using members of the American Population Panel (APP) so that important subgroup analyses could be undertaken, and to allow for follow-up interviews to be conducted with sufficient numbers in today’s climate of declining survey responses. The NSASS was designed and enacted by Prof. Chris Knoester (PI), with support from the College of Arts and Sciences, the Sports and Society Initiative, and CHRR at The Ohio State University. CHRR worked closely with the PI to design the study, navigate Institutional Review Board protocols, construct and polish the survey instrument, program the online interface, and collect, document and disseminate the data. This document summarizes the survey design and data collection efforts of the NSASS.

About this title - Geoethics: Status and Future Perspectives

10.1144/sp508 ◽  
2021 ◽  
Vol 508 (1) ◽  
pp. NP-NP
Author(s):  
G. Di Capua ◽  
P. T. Bobrowsky ◽  
S. W. Kieffer ◽  
C. Palinkas
Keyword(s):  
Common Ground ◽  
Decision Makers ◽  
Ethical Framework ◽  
Global Ethics ◽  
Social Scientists ◽  
Anthropogenic Changes ◽  
The Earth ◽  
Cultural Implications ◽  
Significant Step ◽  
Additional Service

This is the second volume focused on geoethics published by the Geological Society of London. This is a significant step forward in which authors address the maturation of geoethics. The field of geoethics is now ready to be introduced outside the geoscience community as a logical platform for global ethics that addresses anthropogenic changes. Geoethics has a distinction in the geoscientific community for discussing ethical, social and cultural implications of geoscience knowledge, research, practice, education and communication. This provides a common ground for confronting ideas, experiences and proposals on how geosciences can supply additional service to society in order to improve the way humans interact responsibly with the Earth system. This book provides new messages to geoscientists, social scientists, intellectuals, law- and decision-makers, and laypeople. Motivations and actions for facing global anthropogenic changes and their intense impacts on the planet need to be governed by an ethical framework capable of merging a solid conceptual structure with pragmatic approaches based on geoscientific knowledge. This philosophy defines geoethics.

Imperatives for Theories in Social Science: The North-South Divide Notwithstanding

2020 ◽  
Vol 19 (3) ◽  
pp. 195-217
Author(s):  
Aaron Ola Ogundiwin, ◽  
Joel N. Nwachukwu
Keyword(s):  
Social Science ◽  
Conceptual Analysis ◽  
Social Research ◽  
Science Data ◽  
Social Scientists ◽  
The North ◽  
Common Mistake ◽  
History Of ◽  
Research Questions ◽  
Social Science Data

Abstract The paper underscores the place of theories in organizing social science data and experience. It holds that theories are indispensable to social research (The North-South divide notwithstanding), in view of the fact that the framework of knowledge and experience within which theories are established make a meaningful explanation of the world phenomenon reasonably possible. It delineates political philosophy and history of ideas from theory and thus, takes care of common mistake social scientists make differentiating between them. Furthermore, the paper on one hand, takes on the scientific requisites of theory such as assumption, concepts (and their functions), hypothesis (and its characteristics typology), law, models, paradigm and provides lucid conceptual analysis of each with a view to showing their relatedness to theory but not as synonyms to it. On the other hand, we singled out dependency theory in its emanation from knowledge and experience of underdevelopment of Third World countries, as the first and perhaps most relevant theoretic explanation of Africa’s underdevelopment. The paper posits that a good theory that will serve as a rudder for formulation of research questions, problem statement, as well as sustain the data analysis, and findings must parade some, if not all of the following qualities: precision and testability, empirical validity, parsimony, stimulation, and practicability.

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