Convergence and Inequality in Research Globalization
- URL: http://arxiv.org/abs/2103.02052v1
- Date: Tue, 2 Mar 2021 22:04:24 GMT
- Title: Convergence and Inequality in Research Globalization
- Authors: Saurabh Mishra and Kuansan Wang
- Abstract summary: The catch-up effect and the Matthew effect offer opposing characterizations of globalization.
We conduct an in-depth study based on scholarly and patent publications covering STEM research from 218 countries/regions over the past four decades.
- Score: 6.267366754791155
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: The catch-up effect and the Matthew effect offer opposing characterizations
of globalization: the former predicts an eventual convergence as the poor can
grow faster than the rich due to free exchanges of complementary resources,
while the latter, a deepening inequality between the rich and the poor. To
understand these effects on the globalization of research, we conduct an
in-depth study based on scholarly and patent publications covering STEM
research from 218 countries/regions over the past four decades, covering more
than 55 million scholarly articles and 1.7 billion citations. Unique to this
investigation is the simultaneous examination of both the research output and
its impact in the same data set, using a novel machine learning based measure,
called saliency, to mitigate the intrinsic biases in quantifying the research
impact. The results show that the two effects are in fact co-occurring: there
are clear indications of convergence among the high income and upper middle
income countries across the STEM fields, but a widening gap is developing that
segregates the lower middle and low income regions from the higher income
regions. Furthermore, the rate of convergence varies notably among the STEM
sub-fields, with the highly strategic area of Artificial Intelligence (AI)
sandwiched between fields such as Medicine and Materials Science that occupy
the opposite ends of the spectrum. The data support the argument that a leading
explanation of the Matthew effect, namely, the preferential attachment theory,
can actually foster the catch-up effect when organizations from lower income
countries forge substantial research collaborations with those already
dominant. The data resoundingly show such collaborations benefit all parties
involved, and a case of role reversal can be seen in the Materials Science
field where the most advanced signs of convergence are observed.
Related papers
- PORCA: Root Cause Analysis with Partially Observed Data [15.007249208547885]
Root Cause Analysis (RCA) aims at identifying the underlying causes of system faults by uncovering and analyzing the causal structure from complex systems.
Previous studies implicitly assume a full observation of the system, which neglect the effect of partial observation.
We propose PORCA, a novel RCA framework which can explore reliable root causes under both unobserved confounders and unobserved heterogeneity.
arXiv Detail & Related papers (2024-07-08T12:31:12Z) - Practical Guide for Causal Pathways and Sub-group Disparity Analysis [1.8974791957167259]
We use causal disparity analysis to quantify and examine the causal interplay between sensitive attributes and outcomes.
Our two-step investigation focuses on datasets where race serves as the sensitive attribute.
We demonstrate that the sub-groups identified by our approach to be affected the most by disparities are the ones with the largest ML classification errors.
arXiv Detail & Related papers (2024-07-02T22:51:01Z) - Unveil the Duality of Retrieval-Augmented Generation: Theoretical Analysis and Practical Solution [76.75124161306795]
Retrieval-augmented generation (RAG) utilizes retrieved texts to enhance large language models (LLMs)
This paper takes the first step in theoretically giving the essential explanation of benefit and detriment in RAG.
Based on our theory, we propose a practical novel method, X-RAG, which achieves collaborative generation between pure LLM and RAG at token level.
arXiv Detail & Related papers (2024-06-03T02:56:14Z) - Reduced-Rank Multi-objective Policy Learning and Optimization [57.978477569678844]
In practice, causal researchers do not have a single outcome in mind a priori.
In government-assisted social benefit programs, policymakers collect many outcomes to understand the multidimensional nature of poverty.
We present a data-driven dimensionality-reduction methodology for multiple outcomes in the context of optimal policy learning.
arXiv Detail & Related papers (2024-04-29T08:16:30Z) - Evolving landscape of US-China science collaboration: Convergence and
divergence [0.0]
The US and China have significantly fortified their collaboration across diverse scientific disciplines.
Recent reports hint at a potential decline in collaboration between these two giants.
This study delves into the evolving landscape of interaction between the US and China over recent decades.
arXiv Detail & Related papers (2023-09-10T14:11:46Z) - Fairness in Recommender Systems: Research Landscape and Future
Directions [119.67643184567623]
We review the concepts and notions of fairness that were put forward in the area in the recent past.
We present an overview of how research in this field is currently operationalized.
Overall, our analysis of recent works points to certain research gaps.
arXiv Detail & Related papers (2022-05-23T08:34:25Z) - Cascaded Debiasing: Studying the Cumulative Effect of Multiple
Fairness-Enhancing Interventions [48.98659895355356]
This paper investigates the cumulative effect of multiple fairness enhancing interventions at different stages of the machine learning (ML) pipeline.
Applying multiple interventions results in better fairness and lower utility than individual interventions on aggregate.
On the downside, fairness-enhancing interventions can negatively impact different population groups, especially the privileged group.
arXiv Detail & Related papers (2022-02-08T09:20:58Z) - Identifying Causal Influences on Publication Trends and Behavior: A Case
Study of the Computational Linguistics Community [10.791197825505755]
We present mixed-method analyses to investigate causal influences of publication trends and behavior.
Key findings highlight the transition to rapidly emerging methodologies in the research community.
We anticipate this work to provide useful insights about publication trends and behavior.
arXiv Detail & Related papers (2021-10-15T08:36:13Z) - Towards Unbiased Visual Emotion Recognition via Causal Intervention [63.74095927462]
We propose a novel Emotion Recognition Network (IERN) to alleviate the negative effects brought by the dataset bias.
A series of designed tests validate the effectiveness of IERN, and experiments on three emotion benchmarks demonstrate that IERN outperforms other state-of-the-art approaches.
arXiv Detail & Related papers (2021-07-26T10:40:59Z) - Interdisciplinary research and technological impact: Evidence from
biomedicine [2.741266294612776]
We study one aspect of societal benefits that is contributing to the development of patented technologies.
We measure the degree of interdisciplinarity of a paper using three popular indicators, namely variety, balance, and disparity.
Our work may have policy implications for interdisciplinary research and scientific and technological impact.
arXiv Detail & Related papers (2020-06-27T15:21:40Z) - A Survey on Causal Inference [64.45536158710014]
Causal inference is a critical research topic across many domains, such as statistics, computer science, education, public policy and economics.
Various causal effect estimation methods for observational data have sprung up.
arXiv Detail & Related papers (2020-02-05T21:35:29Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.