English
 
Help Privacy Policy Disclaimer
  Advanced SearchBrowse

Item

 
 
DownloadE-Mail

Released

Journal Article

The Network of Early Modern Printers and Its Impact on the Evolution of Scientific Knowledge: Automatic Detection of Awareness Relationships

MPS-Authors
/persons/resource/persons194421

Valleriani,  Matteo       
Department Structural Changes in Systems of Knowledge, Max Planck Institute for the History of Science, Max Planck Society;

/persons/resource/persons196920

Vogl,  Malte       
Department Structural Changes in Systems of Knowledge, Max Planck Institute for the History of Science, Max Planck Society;

/persons/resource/persons258730

El-Hajj,  Hassan
Department Structural Changes in Systems of Knowledge, Max Planck Institute for the History of Science, Max Planck Society;

/persons/resource/persons256457

Pham,  Kim       
Research IT, Max Planck Institute for the History of Science, Max Planck Society;

External Resource
Fulltext (restricted access)
There are currently no full texts shared for your IP range.
Fulltext (public)

histories-02-00033.pdf
(Publisher version), 26MB

Supplementary Material (public)
There is no public supplementary material available
Citation

Valleriani, M., Vogl, M., El-Hajj, H., & Pham, K. (2022). The Network of Early Modern Printers and Its Impact on the Evolution of Scientific Knowledge: Automatic Detection of Awareness Relationships. Histories, 2(4), 466-503. doi:10.3390/histories2040033.


Cite as: https://hdl.handle.net/21.11116/0000-000B-75A0-C
Abstract
This work describes a computational method for reconstructing clusters of social relationships among early modern printers and publishers, the most determinant agents for the process of transformation of scientific knowledge. The method is applied to a dataset retrieved from the Sphaera corpus, a collection of 359 editions of textbooks used at European universities and produced between the years 1472 and 1650. The method makes use of standard bibliographic data and fingerprints; social relationships are defined as “awareness relationships”. The historical background is constituted of the production and economic practices of early modern printers and publishers in the academic book market. The work concludes with empirically validating historical case studies, their historical interpretation, and suggestions for further improvements by utilizing machine learning technologies.