Measuring Disagreement in Science
- URL: http://arxiv.org/abs/2107.14641v1
- Date: Fri, 30 Jul 2021 14:07:34 GMT
- Title: Measuring Disagreement in Science
- Authors: Wout S. Lamers (1), Kevin Boyack (2), Vincent Larivi\`ere (3), Cassidy
R. Sugimoto (4), Nees Jan van Eck (1), Ludo Waltman (1), Dakota Murray (4)
((1) Centre for Science and Technology Studies, Leiden University, Leiden,
Netherlands, (2) SciTech Strategies, Inc., Albuquerque, NM, USA, (3) \'Ecole
de biblioth\'economie et des sciences de l'information, Universit\'e de
Montr\'eal, Canada, (4) School of Informatics, Computing, and Engineering,
Indiana University Bloomington, IN, USA)
- Abstract summary: We use a cue-phrase based approach to identify instances of disagreement citations across more than four million scientific articles.
We reveal a disciplinary spectrum of disagreement, with higher disagreement in the social sciences and lower disagreement in physics and mathematics.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Disagreement is essential to scientific progress. However, the extent of
disagreement in science, its evolution over time, and the fields in which it
happens, remains largely unknown. Leveraging a massive collection of scientific
texts, we develop a cue-phrase based approach to identify instances of
disagreement citations across more than four million scientific articles. Using
this method, we construct an indicator of disagreement across scientific fields
over the 2000-2015 period. In contrast with black-box text classification
methods, our framework is transparent and easily interpretable. We reveal a
disciplinary spectrum of disagreement, with higher disagreement in the social
sciences and lower disagreement in physics and mathematics. However, detailed
disciplinary analysis demonstrates heterogeneity across sub-fields, revealing
the importance of local disciplinary cultures and epistemic characteristics of
disagreement. Paper-level analysis reveals notable episodes of disagreement in
science, and illustrates how methodological artefacts can confound analyses of
scientific texts. These findings contribute to a broader understanding of
disagreement and establish a foundation for future research to understanding
key processes underlying scientific progress.
Related papers
- Towards a Theoretical Foundation of Process Science [1.4372498385359374]
Process science is a highly interdisciplinary field of research.
Despite numerous proposals, process science lacks an adequate understanding of the core concepts of the field.
A more systematic framework to cope with process science is mandatory.
arXiv Detail & Related papers (2024-03-28T08:19:39Z) - Understanding Fine-grained Distortions in Reports of Scientific Findings [46.96512578511154]
Distorted science communication harms individuals and society as it can lead to unhealthy behavior change and decrease trust in scientific institutions.
Given the rapidly increasing volume of science communication in recent years, a fine-grained understanding of how findings from scientific publications are reported to the general public is crucial.
arXiv Detail & Related papers (2024-02-19T19:00:01Z) - Diverse Explanations From Data-Driven and Domain-Driven Perspectives in the Physical Sciences [4.442043151145212]
This Perspective explores the sources and implications of diverse explanations in machine learning applications for physical sciences.
We examine how different models, explanation methods, levels of feature attribution, and stakeholder needs can result in varying interpretations of ML outputs.
Our analysis underscores the importance of considering multiple perspectives when interpreting ML models in scientific contexts.
arXiv Detail & Related papers (2024-02-01T05:28:28Z) - Scientific Large Language Models: A Survey on Biological & Chemical Domains [47.97810890521825]
Large Language Models (LLMs) have emerged as a transformative power in enhancing natural language comprehension.
The application of LLMs extends beyond conventional linguistic boundaries, encompassing specialized linguistic systems developed within various scientific disciplines.
As a burgeoning area in the community of AI for Science, scientific LLMs warrant comprehensive exploration.
arXiv Detail & Related papers (2024-01-26T05:33:34Z) - Ontology-Driven Processing of Transdisciplinary Domain Knowledge [15.819087559924784]
Modern science is unable to solve real-world problems in a fundamental way.
Noosphere thesis appeals to the scientific worldview that needs to be built in a way that overcomes the interdisciplinary barriers.
arXiv Detail & Related papers (2023-11-01T07:42:34Z) - A Diachronic Analysis of Paradigm Shifts in NLP Research: When, How, and
Why? [84.46288849132634]
We propose a systematic framework for analyzing the evolution of research topics in a scientific field using causal discovery and inference techniques.
We define three variables to encompass diverse facets of the evolution of research topics within NLP.
We utilize a causal discovery algorithm to unveil the causal connections among these variables using observational data.
arXiv Detail & Related papers (2023-05-22T11:08:00Z) - Modeling Information Change in Science Communication with Semantically
Matched Paraphrases [50.67030449927206]
SPICED is the first paraphrase dataset of scientific findings annotated for degree of information change.
SPICED contains 6,000 scientific finding pairs extracted from news stories, social media discussions, and full texts of original papers.
Models trained on SPICED improve downstream performance on evidence retrieval for fact checking of real-world scientific claims.
arXiv Detail & Related papers (2022-10-24T07:44:38Z) - SciTweets -- A Dataset and Annotation Framework for Detecting Scientific
Online Discourse [2.3371548697609303]
Scientific topics, claims and resources are increasingly debated as part of online discourse.
This has led to both significant societal impact and increased interest in scientific online discourse from various disciplines.
Research across disciplines currently suffers from a lack of robust definitions of the various forms of science-relatedness.
arXiv Detail & Related papers (2022-06-15T08:14:55Z) - An Informational Space Based Semantic Analysis for Scientific Texts [62.997667081978825]
This paper introduces computational methods for semantic analysis and the quantifying the meaning of short scientific texts.
The representation of scientific-specific meaning is standardised by replacing the situation representations, rather than psychological properties.
The research in this paper conducts the base for the geometric representation of the meaning of texts.
arXiv Detail & Related papers (2022-05-31T11:19:32Z) - 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) - Semantic Analysis for Automated Evaluation of the Potential Impact of
Research Articles [62.997667081978825]
This paper presents a novel method for vector representation of text meaning based on information theory.
We show how this informational semantics is used for text classification on the basis of the Leicester Scientific Corpus.
We show that an informational approach to representing the meaning of a text has offered a way to effectively predict the scientific impact of research papers.
arXiv Detail & Related papers (2021-04-26T20:37:13Z)
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.