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

• Towards Leveraging News Media to Support Impact Assessment of AI Technologies [3.2566808526538873]
  Expert-driven frameworks for impact assessments (IAs) may inadvertently overlook the effects of AI technologies on the public's social behavior, policy, and the cultural and geographical contexts shaping the perception of AI and the impacts around its use. This research explores the potentials of fine-tuning LLMs on negative impacts of AI reported in a diverse sample of articles from 266 news domains spanning 30 countries around the world to incorporate more diversity into IAs.
  arXiv  Detail & Related papers  (2024-11-04T19:12:27Z)
• Fairness and Bias Mitigation in Computer Vision: A Survey [61.01658257223365]
  Computer vision systems are increasingly being deployed in high-stakes real-world applications. There is a dire need to ensure that they do not propagate or amplify any discriminatory tendencies in historical or human-curated data. This paper presents a comprehensive survey on fairness that summarizes and sheds light on ongoing trends and successes in the context of computer vision.
  arXiv  Detail & Related papers  (2024-08-05T13:44:22Z)
• 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)
• 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)
• Discovery of the Hidden World with Large Language Models [95.58823685009727]
  This paper presents Causal representatiOn AssistanT (COAT) that introduces large language models (LLMs) to bridge the gap. LLMs are trained on massive observations of the world and have demonstrated great capability in extracting key information from unstructured data. COAT also adopts CDs to find causal relations among the identified variables as well as to provide feedback to LLMs to iteratively refine the proposed factors.
  arXiv  Detail & Related papers  (2024-02-06T12:18:54Z)
• 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)
• 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.