This Element will overview research using models to understand scientific practice. Models are useful for reasoning about groups and processes that are complicated and distributed across time and space, i.e., those that are difficult to study using empirical methods alone. Science fits this picture. For this reason, it is no surprise that researchers have turned to models over the last few decades to study various features of science. The different sections of the element are mostly organized around different modeling approaches. The models described in this element sometimes yield take-aways that are straightforward, and at other times more nuanced. The Element ultimately argues that while these models are epistemically useful, the best way to employ most of them to understand and improve science is in combination with empirical methods and other sorts of theorizing.
Author(s): Cailin O'Connor
Series: Elements in the Philosophy of Science
Publisher: Cambridge University Press
Year: 2023
Language: English
City: Cambridge
Cover
Title page
Imprints page
Modeling Scientific Communities
Contents
1 Introduction
2 The Credit Economy
2.1 The Division of Scientific Labor
2.2 Replication
2.3 Fraud and Corner Cutting
2.4 Communism
2.5 Credit and Productivity
2.6 The Matthew Effect
2.7 Peer Review, Journals, and Funding
2.8 Incentives and Gender Identity
3 The Natural Selection of Science
3.1 The Natural Selection of Bad, Good, and Conservative Science
3.2 Self-Preferential Biases and False Paradigms
3.3 Industrial Selection
3.4 Levels of Selection in Science
3.5 The Emergence of Discrimination in Science
4 Social Networks and Scientific Knowledge
4.1 Models of Evidence Sharing
4.2 The Benefits of Transient Diversity
4.2.1 Limiting Communication
4.2.2 Stubbornness and Bias
4.2.3 Grants for Transient Diversity
4.2.4 Demographic Diversity in Science
4.3 Harms of Transient Diversity in Science
4.3.1 Scientific Polarization
4.3.2 Industry, Policymakers, and Networks
4.4 Scientific Network Formation
4.4.1 Preferential Attachment
4.4.2 Homophily
5 Epistemic Landscapes
5.1 Epistemic Landscapes and Exploration
5.1.1 Lotteries
5.1.2 Interpreting Epistemic Landscapes
5.2 Rugged Epistemic Landscapes
5.3 Diversity Trumps Ability
6 The Replication Crisis and Methodological Reform
6.1 Are Most Findings False?
6.2 Self-correction and Publication Bias
6.3 Questionable Research Practices
6.3.1 P-Hacking
6.3.2 HARKing
6.4 Responses to the Replication Crisis
6.4.1 Changing the Significance Threshold
6.4.2 Reforming NHST
6.4.3 Registered Reports, Journals for Null Results, and Preregistration
6.4.4 Transparency in Practice
6.4.5 Improving Theory
6.4.6 Multiple Labs and Adversarial Collaborations
7 Conclusion
References
Acknowledgements
