scholarly journals The Diversity–Innovation Paradox in Science

2020 ◽  
Vol 117 (17) ◽  
pp. 9284-9291 ◽  
Author(s):  
Bas Hofstra ◽  
Vivek V. Kulkarni ◽  
Sebastian Munoz-Najar Galvez ◽  
Bryan He ◽  
Dan Jurafsky ◽  
...  
Keyword(s):  
Machine Learning ◽  
Text Analysis ◽  
Racial Minorities ◽  
Academic Careers ◽  
Prior Work ◽  
Underrepresented Groups ◽  
Scientific Careers ◽  
Faculty Positions ◽  
Majority Groups ◽  
Scientific Innovations

Prior work finds a diversity paradox: Diversity breeds innovation, yet underrepresented groups that diversify organizations have less successful careers within them. Does the diversity paradox hold for scientists as well? We study this by utilizing a near-complete population of ∼1.2 million US doctoral recipients from 1977 to 2015 and following their careers into publishing and faculty positions. We use text analysis and machine learning to answer a series of questions: How do we detect scientific innovations? Are underrepresented groups more likely to generate scientific innovations? And are the innovations of underrepresented groups adopted and rewarded? Our analyses show that underrepresented groups produce higher rates of scientific novelty. However, their novel contributions are devalued and discounted: For example, novel contributions by gender and racial minorities are taken up by other scholars at lower rates than novel contributions by gender and racial majorities, and equally impactful contributions of gender and racial minorities are less likely to result in successful scientific careers than for majority groups. These results suggest there may be unwarranted reproduction of stratification in academic careers that discounts diversity’s role in innovation and partly explains the underrepresentation of some groups in academia.

Internationalization and Diversification of Academic Careers

2018 ◽  
Author(s):  
Sorana Toma ◽  
Maria Villares-Varela
Keyword(s):  
Intrinsic Motivation ◽  
Work Experience ◽  
International Study ◽  
Career Progression ◽  
Academic Careers ◽  
Research Environment ◽  
Scientific Careers ◽  
Global Survey ◽  
Research Facilities ◽  
Depth Interviews

This chapter examines the major patterns and drivers of interlinked geographical and career mobilities of Indian-born researchers and scientists. Based on a global survey and in-depth interviews, this study shows that the mobility of Indian researchers is mainly driven by an intrinsic motivation to internationalize their scientific careers, but has also to do with the characteristics of the research environment in India. Moving abroad enables researchers to acquire expertise in a field of research that is not sufficiently developed back home, and provides exposure to research facilities and personnel deemed better and more qualified than those back home. In this respect, international study and work experience are often perceived as providing professional merits that are instrumental in career progression on return to India.

Summaries

2018 ◽  
Vol 46 (1) ◽  
Keyword(s):  
Machine Learning ◽  
Content Analysis ◽  
Text Analysis ◽  
English Language ◽  
Automated Analysis ◽  
Supervised Machine Learning ◽  
Automated Text Analysis ◽  
Research Problems ◽  
Dutch Language ◽  
Automated Methods

Damian Trilling & Jelle Boumans Automated analysis of Dutch language-based texts. An overview and research agenda While automated methods of content analysis are increasingly popular in today’s communication research, these methods have hardly been adopted by communication scholars studying texts in Dutch. This essay offers an overview of the possibilities and current limitations of automated text analysis approaches in the context of the Dutch language. Particularly in dictionary-based approaches, research is far less prolific as research on the English language. We divide the most common types of content-analytical research questions into three categories: 1) research problems for which automated methods ought to be used, 2) research problems for which automated methods could be used, and 3) research problems for which automated methods (currently) cannot be used. Finally, we give suggestions for the advancement of automated text analysis approaches for Dutch texts. Keywords: automated content analysis, Dutch, dictionaries, supervised machine learning, unsupervised machine learning

A content spectral-based text representation

2021 ◽  
pp. 1-12
Author(s):  
Melesio Crespo-Sanchez ◽  
Ivan Lopez-Arevalo ◽  
Edwin Aldana-Bobadilla ◽  
Alejandro Molina-Villegas
Keyword(s):  
Machine Learning ◽  
Text Analysis ◽  
Question Answering ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Text Representation ◽  
Feature Vectors ◽  
Learning Tasks ◽  
Semantic Component ◽  
Vector Representations

In the last few years, text analysis has grown as a keystone in several domains for solving many real-world problems, such as machine translation, spam detection, and question answering, to mention a few. Many of these tasks can be approached by means of machine learning algorithms. Most of these algorithms take as input a transformation of the text in the form of feature vectors containing an abstraction of the content. Most of recent vector representations focus on the semantic component of text, however, we consider that also taking into account the lexical and syntactic components the abstraction of content could be beneficial for learning tasks. In this work, we propose a content spectral-based text representation applicable to machine learning algorithms for text analysis. This representation integrates the spectra from the lexical, syntactic, and semantic components of text producing an abstract image, which can also be treated by both, text and image learning algorithms. These components came from feature vectors of text. For demonstrating the goodness of our proposal, this was tested on text classification and complexity reading score prediction tasks obtaining promising results.

Academic careers workshop for underrepresented groups

2013 ◽  
Vol 56 (3) ◽  
pp. 27-29 ◽  
Author(s):  
Denice Ward Hood ◽  
Stafford Hood ◽  
Dominica McBride
Keyword(s):  
Academic Careers ◽  
Underrepresented Groups

Machine learning based text analysis for intelligence collation

2005 ◽  
Author(s):  
C. Thie ◽  
Z. Lock ◽  
D. Smith ◽  
E. Cribb ◽  
A. Ford ◽  
...  
Keyword(s):  
Machine Learning ◽  
Text Analysis

scholarly journals In the Pursuit of Privacy: The Promises and Predicaments of Federated Learning in Healthcare

2021 ◽  
Vol 4 ◽  
Author(s):  
Mustafa Y. Topaloglu ◽  
Elisabeth M. Morrell ◽  
Suraj Rajendran ◽  
Umit Topaloglu
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Dice Coefficient ◽  
New Paradigm ◽  
Underrepresented Groups ◽  
Patient Protection ◽  
Shapley Values ◽  
Geographically Distributed ◽  
Computationally Expensive ◽  
Improved Performance

Artificial Intelligence and its subdomain, Machine Learning (ML), have shown the potential to make an unprecedented impact in healthcare. Federated Learning (FL) has been introduced to alleviate some of the limitations of ML, particularly the capability to train on larger datasets for improved performance, which is usually cumbersome for an inter-institutional collaboration due to existing patient protection laws and regulations. Moreover, FL may also play a crucial role in circumventing ML’s exigent bias problem by accessing underrepresented groups’ data spanning geographically distributed locations. In this paper, we have discussed three FL challenges, namely: privacy of the model exchange, ethical perspectives, and legal considerations. Lastly, we have proposed a model that could aide in assessing data contributions of a FL implementation. In light of the expediency and adaptability of using the Sørensen–Dice Coefficient over the more limited (e.g., horizontal FL) and computationally expensive Shapley Values, we sought to demonstrate a new paradigm that we hope, will become invaluable for sharing any profit and responsibilities that may accompany a FL endeavor.

scholarly journals Dream to explore: 5-HT2a as adaptive temperature parameter for sophisticated affective inference

2021 ◽  
Author(s):  
Adam Safron ◽  
Zahra Sheikhbahaee
Keyword(s):  
Machine Learning ◽  
Prior Work ◽  
Active Inference ◽  
Policy Selection ◽  
Temperature Parameter ◽  
Uncertainty Parameter ◽  
Absorbing States

Relative to other neuromodulators, serotonin (5-HT) has received far less attention in machine learning and active inference. We will review prior work interpreting 5-HT1a signaling as an uncertainty parameter with opponency to dopamine. We will then discuss how 5-HT2a receptors may promote more exploratory policy selection by enhancing imaginative planning (as sophisticated affective inference). Finally, we will briefly comment on how qualitatively different effects may be observed across low and high levels of 5-HT2a signaling, where the latter may help agents to change self-adversarial policies and break free of maladaptive absorbing states in POMDPs.

scholarly journals ActiveThief: Model Extraction Using Active Learning and Unannotated Public Data

2020 ◽  
Vol 34 (01) ◽  
pp. 865-872
Author(s):  
Soham Pal ◽  
Yash Gupta ◽  
Aditya Shukla ◽  
Aditya Kanade ◽  
Shirish Shevade ◽  
...  
Keyword(s):  
Machine Learning ◽  
Active Learning ◽  
Domain Knowledge ◽  
Third Party ◽  
Prior Work ◽  
Model Extraction ◽  
Public Data ◽  
Adversarial Examples ◽  
Application Programming ◽  
Public Datasets

Machine learning models are increasingly being deployed in practice. Machine Learning as a Service (MLaaS) providers expose such models to queries by third-party developers through application programming interfaces (APIs). Prior work has developed model extraction attacks, in which an attacker extracts an approximation of an MLaaS model by making black-box queries to it. We design ActiveThief – a model extraction framework for deep neural networks that makes use of active learning techniques and unannotated public datasets to perform model extraction. It does not expect strong domain knowledge or access to annotated data on the part of the attacker. We demonstrate that (1) it is possible to use ActiveThief to extract deep classifiers trained on a variety of datasets from image and text domains, while querying the model with as few as 10-30% of samples from public datasets, (2) the resulting model exhibits a higher transferability success rate of adversarial examples than prior work, and (3) the attack evades detection by the state-of-the-art model extraction detection method, PRADA.

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