The Concept of Decentralization Through Time and Disciplines: A
Quantitative Exploration
- URL: http://arxiv.org/abs/2207.14260v2
- Date: Fri, 6 Oct 2023 10:24:31 GMT
- Title: The Concept of Decentralization Through Time and Disciplines: A
Quantitative Exploration
- Authors: Gabriele Di Bona, Alberto Bracci, Nicola Perra, Vito Latora, Andrea
Baronchelli
- Abstract summary: We analyse 425,144 academic publications that refer to (de)centralization.
We find that the fraction of papers on the topic has been exponentially increasing since the 1950s.
In 2021, 1 author in 154 mentioned (de)centralization in the title or abstract of an article.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Decentralization is a pervasive concept found across disciplines, including
Economics, Political Science, and Computer Science, where it is used in
distinct yet interrelated ways. Here, we develop and publicly release a general
pipeline to investigate the scholarly history of the term, analysing 425,144
academic publications that refer to (de)centralization. We find that the
fraction of papers on the topic has been exponentially increasing since the
1950s. In 2021, 1 author in 154 mentioned (de)centralization in the title or
abstract of an article. Using both semantic information and citation patterns,
we cluster papers in fields and characterize the knowledge flows between them.
Our analysis reveals that the topic has independently emerged in the different
fields, with small cross-disciplinary contamination. Moreover, we show how
Blockchain has become the most influential field about 10 years ago, while
Governance dominated before the 1990s. In summary, our findings provide a
quantitative assessment of the evolution of a key yet elusive concept, which
has undergone cycles of rise and fall within different fields. Our pipeline
offers a powerful tool to analyze the evolution of any scholarly term in the
academic literature, providing insights into the interplay between collective
and independent discoveries in science.
Related papers
- Data-driven Coreference-based Ontology Building [48.995395445597225]
Coreference resolution is traditionally used as a component in individual document understanding.
We take a more global view and explore what can we learn about a domain from the set of all document-level coreference relations.
We release the coreference chains resulting under a creative-commons license, along with the code.
arXiv Detail & Related papers (2024-10-22T14:30:40Z) - Scito2M: A 2 Million, 30-Year Cross-disciplinary Dataset for Temporal Scientometric Analysis [11.672477198630574]
We introduce Scito2M, a longitudinal scientometric dataset with over two million academic publications.
Using Scito2M, we conduct a temporal study spanning over 30 years to explore key questions in scientometrics.
arXiv Detail & Related papers (2024-10-12T12:16:57Z) - Federated Learning for Generalization, Robustness, Fairness: A Survey
and Benchmark [55.898771405172155]
Federated learning has emerged as a promising paradigm for privacy-preserving collaboration among different parties.
We provide a systematic overview of the important and recent developments of research on federated learning.
arXiv Detail & Related papers (2023-11-12T06:32:30Z) - How Do Transformers Learn Topic Structure: Towards a Mechanistic
Understanding [56.222097640468306]
We provide mechanistic understanding of how transformers learn "semantic structure"
We show, through a combination of mathematical analysis and experiments on Wikipedia data, that the embedding layer and the self-attention layer encode the topical structure.
arXiv Detail & Related papers (2023-03-07T21:42:17Z) - Foundations and Recent Trends in Multimodal Machine Learning:
Principles, Challenges, and Open Questions [68.6358773622615]
This paper provides an overview of the computational and theoretical foundations of multimodal machine learning.
We propose a taxonomy of 6 core technical challenges: representation, alignment, reasoning, generation, transference, and quantification.
Recent technical achievements will be presented through the lens of this taxonomy, allowing researchers to understand the similarities and differences across new approaches.
arXiv Detail & Related papers (2022-09-07T19:21:19Z) - Overview of STEM Science as Process, Method, Material, and Data Named
Entities [0.0]
We develop and analyze a large-scale structured dataset of STEM articles across 10 different disciplines.
Our analysis is defined over a large-scale corpus comprising 60K abstracts structured as four scientific entities process, method, material, and data.
The STEM-NER-60k corpus, created in this work, comprises over 1M extracted entities from 60k STEM articles obtained from a major publishing platform.
arXiv Detail & Related papers (2022-05-24T07:35:24Z) - Change Summarization of Diachronic Scholarly Paper Collections by
Semantic Evolution Analysis [10.554831859741851]
We demonstrate a novel approach to analyze the collections of research papers published over longer time periods.
Our approach is based on comparing word semantic representations over time and aims to support users in a better understanding of large domain-focused archives of scholarly publications.
arXiv Detail & Related papers (2021-12-07T11:15:19Z) - Scientometric engineering: Exploring citation dynamics via arXiv eprints [0.0]
We investigate the citation data of more than 1.5 million eprints on arXiv.
We find that the typical growth and obsolescence patterns vary across disciplines.
We derive a model consistent with the observed quantitative and temporal characteristics of citation growth and obsolescence.
arXiv Detail & Related papers (2021-06-09T12:38:44Z) - Domain Generalization: A Survey [146.68420112164577]
Domain generalization (DG) aims to achieve OOD generalization by only using source domain data for model learning.
For the first time, a comprehensive literature review is provided to summarize the ten-year development in DG.
arXiv Detail & Related papers (2021-03-03T16:12:22Z) - What's New? Summarizing Contributions in Scientific Literature [85.95906677964815]
We introduce a new task of disentangled paper summarization, which seeks to generate separate summaries for the paper contributions and the context of the work.
We extend the S2ORC corpus of academic articles by adding disentangled "contribution" and "context" reference labels.
We propose a comprehensive automatic evaluation protocol which reports the relevance, novelty, and disentanglement of generated outputs.
arXiv Detail & Related papers (2020-11-06T02:23:01Z) - How do academic topics shift across altmetric sources? A case study of
the research area of Big Data [2.208242292882514]
Author keywords from publications and terms from online events are extracted as the main topics of the publications and the online discussion of their audiences at Altmetric.
Results show there are substantial differences between the two sets of topics around Big Data scientific research.
Blogs and News show a strong similarity in the terms commonly used, while Policy documents and Wikipedia articles exhibit the strongest dissimilarity in considering and interpreting Big Data related research.
arXiv Detail & Related papers (2020-03-23T19:37:36Z)
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.